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COVID‐19 has been ravaging the world for a long time, and although its effects are currently the same as those of a cold or a fever, timely diagnosis of COVID‐19 in the elderly and in patients with related illnesses is still a matter of great urgency. To address this challenge, we propose a model that combines the strengths of the Swin Transformer and ResNet34 architectures to efficiently diagnose COVID‐19 in elderly and vulnerable patients. In this paper, we design a model that integrates Swin transformer and resnet34, which not only integrates the advantages of transformer and CNN but also achieves excellent performance in this image classification problem. Moreover, a pre‐processing method is also proposed to increase the accuracy of the model to 99.08%. In this paper, experiments were conducted on Kaggle's publicly available three‐classification and four‐classification datasets, respectively, and on the three main evaluation metrics of Accuracy, Precision, and Recall, the first dataset obtained 98.81%, 99.49%, and 97.99%, while the second dataset obtained 88.82%, 88.92%, and 86.38%. These findings highlight the validity and potential of our proposed model for diagnosing the presence or absence of COVID‐19 in elderly and vulnerable patients. [ABSTRACT FROM AUTHOR]

In recent years, deep learning techniques have been increasingly utilized to automate and reduce human errors in leukocyte classification. However, reliability and accuracy issues remain a challenge. This paper proposes CAB‐Net, an end‐to‐end deep learning‐based model that integrates a convolutional neural network with an attention mechanism and BLSTM. The model uses a stack of convolutional and pooling layers to extract initial features from input images, which are then fed into the attention block to generate class‐specific discriminative features and BLSTM employed to model deterministic class correlations between highlighted discriminative features and labels. Experimental results show that CAB‐Net outperforms state‐of‐the‐art methods in term of accuracy, precision, recall, F1‐Score, and AUC‐ROC. In addition, the CAB‐Net model demonstrates good performance even with low‐resolution images. Consequently, CAB‐Net can be considered as the current state‐of‐the‐art prediction model for improving leukocyte classification in clinical environments. [ABSTRACT FROM AUTHOR]

Fetal abdominal subcutaneous soft tissue thickness (FASSTT) is a key indicator in evaluating fetal growth, development, and nutritional status. Currently, manual measurement in FASSTT faces the problems of difficulty in positioning, time consumption, and inaccurate measurement. Therefore, this article proposes an automatic measurement scheme for FASSTT. Firstly, a U‐shaped attention network VGG‐SeUnet is proposed to automatically segment the subcutaneous soft tissue area of the fetal abdomen. Secondly, based on the segmentation results, a morphological method is proposed to obtain FASSTT. Specifically, the segmentation network uses VGG16 as the encoder and connects the decoder through jump connections for multi‐scale fusion. We introduce channel attention to jump connections, which enables the model to select key channels for feature fusion. At the same time, the model uses the proposed DF_Loss function for training to solve the problem of sample imbalance. Based on the segmentation results, a morphological distance transformation algorithm is proposed to obtain FASSTT by drawing the maximum inscribed circle and calculating its diameter. The method is evaluated on a fetal abdominal circumference ultrasound dataset containing 135 samples. The DSC, mIOU, mPA, Precision and Recall of the segmentation experiments reache 88.9%, 81.02%, 90.44%, 86.38%, and 90.44% respectively. The difference between FASSTT's result from that provided by radiologist is 0.0615 cm in Average Error (AVE) and 0.081 cm in Root Mean Square Error (RMSE). The overall performance of this algorithm is superior to existing methods with excellent performance, and the measurement error is less than 1 millimeter. This result proves that the proposed scheme in this paper can achieve automated measurement of FASSTT and assist doctors in subsequent evaluation of fetal development and nutritional status. [ABSTRACT FROM AUTHOR]

With rapid developments in convolutional neural networks for image processing, deep learning methods based on pixel classification have been extensively applied in medical image segmentation. One popular strategy for such tasks is the encoder‐decoder‐based U‐Net architecture and its variants. Most segmentation methods based on fully convolutional networks will cause the loss of spatial and contextual information due to continuous pooling operations or strided convolution when decreasing image resolution, and make less use of contextual information and global information under different receptive fields. To overcome this shortcoming, this paper proposes a novel structure called RAAU‐Net. In our proposed RAAU‐Net structure, which is a modified U‐shaped architecture, we aim to capture high‐level information while preserving spatial information and focusing on the regions of interest. RAAU‐Net comprises three main components: a feature encoder module that utilizes a pre‐trained ResNet‐18 model as a fixed feature extractor, a multi‐receptive field extraction module that we developed, and a feature decoder module. We have tested our method on several 2D medical image segmentation tasks such as retinal nerve, breast tumor, skin lesion, lung, gland, and polyp segmentation. All the indexes of the model reached the best in the dataset of skin lesions, in which Accuracy, Precision, IoU, Recall, and Dice Score were 3.26%, 5.42%, 9.92%, 6.52%, and 5.95% higher than UNet. [ABSTRACT FROM AUTHOR]

To aid in detection of tuberculosis, researchers have concentrated on developing computer‐aided diagnostic technologies based on x‐ray imaging. Since it generates noninvasive standard‐of‐care data, a chest x‐ray image is one of the most often used diagnostic imaging modalities in computer‐aided solutions. Due to their significant interclass similarities and low intra‐class variation abnormalities, chest x‐ray pictures continue to pose difficulty for proper diagnosis. In this paper, a novel automated framework is proposed for the classification of tuberculosis, COVID‐19, and pneumonia from chest x‐ray images using deep learning and improved optimization technique. Two pre‐trained convolutional neural network models such as EfficientB0 and ResNet50 have been utilized and fine‐tuned based on the additional layers. Both models are trained with fixed hyperparameters on the selected datasets and obtained newly trained models. A novel feature selection technique has been proposed that selects the best features. In the novel version, distance and update position formulation has been modified. The selected features are further fused using a novel technique that is based on the serial and standard deviation threshold function. The experimental process of the proposed framework is conducted on three datasets and obtained an accuracy of 98.2%, 99.0%, and 98.7%, respectively. In addition, a detailed Wilcoxon signed‐rank analysis is conducted and shows the proposed method significance performance. Based on the results, it is concluded that the proposed method accuracy is improved after the fusion process. In addition, the comparison with recent techniques shows the proposed method as more significant in terms of accuracy and precision rate. [ABSTRACT FROM AUTHOR]

Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state‐of‐the‐art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi‐scale‐aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor‐based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature‐based method achieved the highest performance for survival days prediction over the state‐of‐the‐art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning‐based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days. [ABSTRACT FROM AUTHOR]

Monkeypox virus has become a popular disease as many people have been recently infected with this virus around the world. This virus causes fears of another global pandemic similar to COVID‐19 pandemic. Therefore, it is important to determine infected people at an early stage to reduce the spread of this virus. Creating an automatic approach to detect monkeypox virus is a challenging problem due to the similar appearance between skin infections. Currently, deep convolutional neural network approaches have been used in detecting and classifying skin infections. However, these approaches sever from certain challenges such as being unable to capture the global context and low accuracy rate. In this paper, we propose a novel approach based on vision transformer to automatically detect monkeypox virus in skin images. We fine‐tune vision transformer for the human monkeypox classification problems. This fine‐tuned vision transformer splits an image into image patches and then these patches are used as input to the transformer in a sequence structure, followed by added layers to detect monkeypox virus. The experimental results show that the proposed approach achieves excellent detection accuracy using a public dataset. [ABSTRACT FROM AUTHOR]

This paper's primary goal is to diagnose COVID‐19 contamination based on the artificial intelligence approach automatically. We used convolutional neural network deep learning algorithm for analyzing the ECG images to detect cardiac abnormalities, consequent of the contamination by the SARS‐CoV‐2 virus, responsible for the COVID‐19 epidemic. We designed, trained, and evaluated the performance of two deep learning models (MobileNetV2 and VGG16) in detecting and distinguishing between two different classes (healthy subjects and COVID‐19 positive cases). Indeed, this virus attacks the human respiratory system, which could affect the heart system. Thus, developing a deep learning model could help for a quick and efficient diagnosis, prediction, and physician decision‐making. The performed deep learning model will be used for predicting abnormal cardiac activities consequent to the contamination by the virus. The overall classification rate achieved by the models was 99.34% and 99.67% for MobileNetV2 and VGG16, respectively. Therefore, this approach can efficiently contribute to the diagnosis of COVID‐19 contamination. [ABSTRACT FROM AUTHOR]

The worldwide spread of the coronavirus (COVID‐19) outbreak has proven devastating to public health. The severity of pneumonia relies on a rapid and accurate diagnosis of COVID‐19 in CT images. Accordingly, a computed tomography (CT) scan is an excellent screening tool for detecting COVID‐19. This paper proposes a deep learning‐based strategy for recognizing and segmenting a COVID‐19 lesion from chest CT images, which would introduce an accurate computer aided decision criteria for the physicians about the severity rate of the patients. Two main stages have been proposed for detecting COVID‐19; first, a convolutional neural network (CNN) deep structure recognizes and classifies COVID‐19 from CT images. Second, a U‐Net deep structure segments the COVID‐19 regions in a semantic manner. The proposed system is trained and evaluated on three different CT datasets for COVID‐19, two of which are used to illustrate the system's segmentation performance and the other is to demonstrate the system's classification ability. Experiment results reveal that the proposed CNN can achieve classification accuracy greater than 0.99, and the proposed U‐Net model outperforms the state‐of‐the‐art in segmentation with an IOU greater than 0.92. [ABSTRACT FROM AUTHOR]

Recently, automatic diagnostic approaches widely use various retinal images to classify ocular diseases. And retinal vein occlusion (RVO) is the second most common retinal vascular disease after diabetic retinopathy. In clinical practice, ophthalmologists are usually accustomed to resorting to images of one modality. But single‐modality images often ignore other modality‐specific information. To solve this problem, this paper uses a novel retinal imaging, the multicolor (MC) imaging, for RVO recognition. It can obtain four multiple modal images with different wavelengths to provide much richer information about retinal features. Since the MC images contain local and global pathologies at multiple scales, a multiscale attention structure is proposed to recognize RVO. In simple terms, this structure uses Resnet as the backbone network for feature extraction, with simultaneous input of images in four modalities. Then, the feature maps at different scales are fed into an attention module to fuse the global and local features, which combines two attention mechanisms, the channel attention mechanism and the spatial attention mechanism. The extensive experimental results demonstrate that our proposed framework achieves quite promising classification performance on the fundus diseases and normal images. [ABSTRACT FROM AUTHOR]

Glioma represents one of the most aggressive cancers, which can develop in the brain. The automatic tumor segmentation and its sub‐regions represent a challenging task owing to their considerable structural variation. It can appear in different ways and with several shapes, which makes tissue identification a crucial task. Reliable and accurate segmentation presents an important component in tumor treatment and diagnosis planning. To overcome these drawbacks, various Deep Learning (DL) schemes are proposed to aid doctors. In this paper, a novel Convolutional Neural Network (CNN) scheme for glioma segmentation was proposed. Our suggested technique consists of three phases. First, we use intensity normalization to ameliorate the image quality as a preprocessing step. Second, an automatic segmentation technique based on CNN has been proposed. The new model has several layers. Finally, and with the goal of refining the segmentation results, we employ a post‐processing approach. We use the public benchmark BraTS datasets from 2018 and 2020 with low‐grade and high‐grade glioma tumors to test the suggested framework. These datasets contain about 285 and 369 patients, respectively. The four modalities are exploited. Each patient has about 155 2D images from every modality. All the images have the same size (240 × 240 pixels). Our technique performs well compared to new methods, with Dice scores of 0.88 for the Whole Tumor, 0.84 for Tumor Core, and 0.71 for Enhancing Tumor based on the first dataset. According to the second dataset, the three regions had an average of 0.88, 0.9, and 0.75, respectively. The Jaccard indexes for the first data set are 0.8, 0.73, and 0.56 for the three regions, respectively. The second data set attains 0.8, 0.82, and 0.6 for the three regions. The results show that the suggested framework is an excellent way to segment data, especially compared to other methods. [ABSTRACT FROM AUTHOR]

Mitral valve (MV) diseases constitute one of the etiologies of cardiovascular mortality and morbidity. MV pathologies need evaluating and classifying via echocardiographic videos. Transformers have significantly advanced video analytics. MV motion is divided by Carpentier functional classification into four types: normal, increased, restricted, and restricted only during systole. This paper introduces CarpNet, a deep transformer network that incorporates video transformers capable of direct MV pathology Carpentier's classification from the parasternal long‐axis (PLA) echocardiographic videos. The network, instead of processing frames independently, analyzes stacks of temporally consecutive frames using multi‐head attention modules to incorporate MV temporal dynamics into the learned model. To that end, different convolutional neural networks (CNNs) are evaluated as the backbone, and the best model is selected using the information of the PLA view. The use of information obtained by our proposed deep transformer network from consecutive echocardiographic frames yielded better results concerning the Carpentier functional classification than information obtained by CNN‐based (single‐frame) models. Using the Inception_Resnet_V2 architecture as the backbone, CarpNet achieved 71% accuracy in the test dataset. Deep learning and transformers in echocardiographic videos can render quick, precise, and stable evaluations of various MV pathologies. [ABSTRACT FROM AUTHOR]

The 2019 coronavirus (COVID‐19), started in China, spreads rapidly around the entire world. In automated medical imagery diagnostic technique, due to presence of noise in medical images and use of single pre‐trained model on low quality images, the existing deep network models cannot provide the optimal results with better accuracy. Hence, hybrid deep learning model of Xception model & Resnet50V2 model is proposed in this paper. This study suggests classifying X‐ray images into three categories namely, normal, bacterial/viral infections and Covid positive. It utilizes CLAHE & BM3D techniques to improve visual clarity and reduce noise. Transfer learning method with variety of pre‐trained models such as ResNet‐50, Inception V3, VGG‐16, VGG‐19, ResNet50V2, and Xception are used for better feature extraction and Chest X‐ray image classification. The overfitting issue were resolved using K‐fold cross validation. The proposed hybrid deep learning model results high accuracy of 97.8% which is better than existing techniques. [ABSTRACT FROM AUTHOR]

The conventional approach for identifying ground glass opacities (GGO) in medical imaging is to use a convolutional neural network (CNN), a subset of artificial intelligence, which provides promising performance in COVID‐19 detection. However, CNN is still limited in capturing structured relationships of GGO as the texture and shape of the GGO can be confused with other structures in the image. In this paper, a novel framework called DeepChestNet is proposed that leverages structured relationships by jointly performing segmentation and classification on the lung, pulmonary lobe, and GGO, leading to enhanced detection of COVID‐19 with findings. The performance of DeepChestNet in terms of dice similarity coefficient is 99.35%, 99.73%, and 97.89% for the lung, pulmonary lobe, and GGO segmentation, respectively. The experimental investigations on DeepChestNet‐Lung, DeepChestNet‐Lobe and DeepChestNet‐COVID datasets, and comparison with several state‐of‐the‐art approaches reveal the great potential of DeepChestNet for diagnosis of COVID‐19 disease. [ABSTRACT FROM AUTHOR]

Over the last few years, researchers have focused on computer‐aided polyp detection in gastroscopy. Deep learning (DL) has shown great promise for polyps' identification. The most exceptional contribution of DL methods in gastroenterology is their ability to identify polyps quickly and accurately using convolution neural network. Nonetheless, despite significant advancements, automatic detection of small polyps remains a challenging and complex task. Furthermore, due to multiple pooling operations, the features of small polyps are lost, resulting in low detection accuracy. This paper proposes an efficient object detection method for polyp detection using gastric images to address this issue. A single‐shot multi‐box detector (SSD) was combined with the feature extractor VGG‐16, and the Refined Map Block (RMB) was integrated into SSD's high‐resolution feature maps to get more semantic information. The RMB output was used as the input to the successive layers. The RMB comprises of attention cascade and feature map concatenation cascade. The attention cascade improved the localization accuracy, while the feature map concatenation cascade improved the classification accuracy. Using the former, the proposed attention‐based SSD for gastric polyps (ASSD‐GPNet) model focused on the specific information, a polyp, rather than the background. Furthermore, the feature map concatenation cascade adds semantic information while reducing computational complexity. The output of these two cascades was combined to produce a refined feature map that enhances the detection of small polyps. The model was trained and tested on 1970 gastric images and Pascal VOC07 + 12. Image augmentation was applied to increase the training data of gastric images to reduce overfitting and skip connections were used to overcome the vanishing gradient problem. Overall, the experimental results demonstrated that the proposed model outperformed than compared models in both medical and natural images. The ASSD‐GPNet obtained mean average precision (mAP) of 94.2% on gastric images and 76.9% on Pascal VOC. [ABSTRACT FROM AUTHOR]

Cancer is more alarming in modern days due to its identification at later stages. Among cancers, lung, liver and colon cancers are the leading cause of untimely death. Manual cancer identification from histopathological images is time‐consuming and labour‐intensive. Thereby, computer‐aided decision support systems are desired. A deep learning model is proposed in this paper to accurately identify cancer. Convolutional neural networks have shown great ability to identify the significant patterns for cancer classification. The proposed Parallel, Cross Concatenated and Grouped Convolutions Deep Neural Network (PC2GCDN2) has been developed to obtain accurate patterns for classification. To prove the robustness of the model, it is evaluated on the KMC and TCGA‐LIHC liver dataset, LC25000 dataset for lung and colon cancer classification. The proposed PC2GCDN2 model outperforms states‐of‐the‐art methods. The model provides 5.5% improved accuracy compared to the LiverNet proposed by Aatresh et. al on the KMC dataset. On the LC25000 dataset, 2% improvement is observed compared to existing models. Performance evaluation metrics like Sensitivity, Specificity, Recall, F1‐Score and Intersection‐Over‐Union are used to evaluate the performance. To the best of our knowledge, PC2GCDN2 can be considered as gold standard for multiple histopathology image classification. PC2GCDN is able to classify the KMC and TCGA‐LIHC liver dataset with 96.4% and 98.6% accuracy, respectively, which are the best results obtained till now. The performance has been superior on LC25000 dataset with 99.5% and 100% classification accuracy on lung and colon dataset, by utilizing less than 0.5 million parameters. [ABSTRACT FROM AUTHOR]

World's science and technologies have been challenged by the COVID‐19 pandemic. Each and every community across the globe are trying to find a real‐time novel method for accurate treatment and cure of COVID‐19 infected patients. The most important lead to take from this pandemic is to detect the infected patients as soon as possible and provide them an accurate treatment. At present, the worldwide methodology to detect COVID‐19 is reverse transcription‐polymerase chain reaction (RT‐PCR). This technique is costly and time taking. For this reason, the implementation of a novel method is required. This paper includes the use of deep learning analysis to develop a system for identifying COVID‐19 patients. Proposed technique is based on convolution neural network (CNN) and deep neural network (DNN). This paper proposes two models, first is designing DNN on the basis of fractal feature of the images and second is designing CNN using lungs x‐ray images. To find the infected area (tissues) of the lungs image using CNN architecture, segmentation process has been used. Developed CNN architecture gave results of classification with accuracy equal to 94.6% and sensitivity equal to 90.5% which is much better than the proposed DNN method, which gave accuracy 84.11% and sensitivity 84.7%. The outcome of the presented model shows 94.6% accuracy in detecting infected regions. Using this method the growth of the infected regions can be monitored and controlled. The designed model can also be used in post‐COVID‐19 analysis. [ABSTRACT FROM AUTHOR]

The feature extraction technique plays a vital role in obtaining better classification accuracy. In this paper, a novel framework is proposed, which develops two‐dimensional (2D) images for convolutional neural network (CNN) to classify four (left hand, right hand, feet, and tongue) MI tasks. 2D image is formed by decomposing each trial using continuous wavelet transform (CWT) filter bank after pre‐processing the MI‐based EEG data by multi‐class common spatial pattern (CSP) method. Obtained images are used to train the CNN model for classification. The proposed framework is evaluated using publicly available BCI competition IV dataset 2a by calculating the classification accuracy for all subjects. Results show that the proposed framework has been giving better classification accuracy than some existing CNN‐based and conventional machine learning‐based approaches compared in this paper. The average time required to train CNN using the proposed framework is 12.67 s, acceptable for online MI‐based BCI applications. [ABSTRACT FROM AUTHOR]

Large bowel obstruction (LBO) occurs when there is a blockage or twisting in the large bowel that prevents wastes and gas from passing through. If left untreated, the blockage cuts off blood supply to the colon, causing sections of it to die which results in high rates of morbidity and fatality. The examination of clinical symptoms of LBO involves careful inspection of the cecum and colon. Radiologists use X‐rays to inspect the clinical signs. Some research has been done to automate the detection of related abdominal and intestinal diseases. However, all these studies concentrate only on detecting Crohn's, ulcerative colitis, Acute Appendicitis, colorectal cancer, celiac diseases, liver diseases, and chronic kidney diseases. Automatic detection and classification of LBO has not been given due attention so far to the best of the authors knowledge. To address this challenge, we have designed a model for the detection and classification of LBO. The models development comprises of stages such as preprocessing, detection, segmentation, feature extraction, and classification. We used YOLOv3 for detection and used a gray scale level co‐occurrence matrix (GLCM), and a convolutional neural network for feature extraction, while support vector machine (SVM) and softmax were used for classification. The proposed model achieved a diagnostic accuracy of 89% when feature extraction methods such as CNN and median filter with softmax classifier were used. CNN and Gaussian filter with soft max classifier achieved 91%, while CNN and anisotropic filter with soft max classifier achieved 92%. GLCM with threshold segmentation and Gaussian filter with SVM classifier achieved 87%, while CNN with watershed segmentation and Gaussian filter with SVM classifier achieved 97% and CNN‐GLCM with watershed segmentation and anisotropic diffusion filter with SVM classifier achieved 98% for detection and classification of LBO. Finally, this paper presented a performance analysis of various machine learning approaches for detection and classification of LBO. Hence, our model is designed to assist human experts (Radiologists) in diagnosing LBO. [ABSTRACT FROM AUTHOR]

Being a deadly disease, breast cancer provides higher global mortality among women. The challenge in employing ultrasound imaging for breast cancer diagnosis lies in the segmentation part. Thus, the work intends to segment the breast cancer ultrasound images using the deep learning approach. The U‐shaped convolution neural network, U‐Net, has become popular and efficient for biomedical image segmentation. Specifically, the paper proposes an improved version, SKMAT‐U‐Net, where a selective kernel (SK) utilizes an attention mechanism to adjust the receptive fields of the network and combine feature maps extricated with dilated and standard convolution operations. Next, based on the conventional cross‐entropy loss function, four attention loss functions are integrated to form the Mixed Attention Loss Function (MAT) based U‐Net. Thus, the SKMAT‐U‐Net model is proposed to segment the lesions effectively in breast ultrasound images. The results obtained indicated that the proposed U‐Net model provides a better Dice score (0.929) than others. [ABSTRACT FROM AUTHOR]

The multi‐organ classification of medical images in disease examination and diagnosis is very valuable. However, some organs features of medical imaging are not obvious, and the organ of interest just occupy several pixels in the image. Therefore, the accuracy of multi‐organ classification of medical images using classic convolutional neural networks (CNN) cannot meet actual requirements. To address the above problems, a Deep Feature Enhancement and Xgboost (DFEX) network is proposed in this paper. The network enhances the discriminability of deep features through the dual‐core CNN feature extraction, the transfer module, and the multi‐level semantic feature fusion module. The dual‐core CNN feature extraction and transfer module extracts high‐level semantic features with detailed information by four aggregation units, to alleviate the problem of inconspicuous imaging features. The multi‐level semantic feature fusion module fuses the low‐level, middle‐level, and high‐level semantic features of the image to obtain more detailed features, which alleviates the problem that the target organ is easily affected by other organs. Finally, the enhanced features are fed to the Xgboost ensemble method for multi‐organ classification tasks of medical images. Our method is compared with 4 different classic networks on 553 cases. The experiment results show our method has better overall performance. Especially, for nasopharynx and vocal fold closing, the classification accuracies of our method achieve 84.39% and 88.00%. Compared with the best performers in classic networks, the accuracies have been increased by 3.58% and 15.62% respectively. [ABSTRACT FROM AUTHOR]

Glaucomatous optic neuropathy is the preeminent cause of incurable vision impairment and blindness across the world. Manual interpretation of the pathological structures in fundus images is time‐consuming and requires the expertise of a competent specialist. With the development of deep learning approaches, automated glaucoma diagnosis is easy and effective for larger screening. Convolutional neural networks, in particular, have emerged as a promising choice for glaucoma detection from fundus images due to their remarkable success in image classification. Transferring the optimized weights from a pre‐trained model expedites and simplifies the training process of deep neural network. In this paper, a deep ensemble model using the stacking ensemble learning technique is developed to attain the optimum performance for the classification of glaucomatous and normal images. Thirteen pre‐trained models such as Alexnet, Googlenet, VGG‐16, VGG‐19, Squeezenet, Resnet‐18, Resnet‐50, Resnet‐101, Efficientnet‐b0, Mobilenet‐v2, Densenet‐201, Inception‐v3, and Xception are implemented. Their performance is compared in 65 different configurations, comprising 13 CNN architectures and five various classification approaches. A two‐stage ensemble selection technique is proposed to select the optimal configurations. Selected configurations are pooled using a probability averaging technique. The final classification is performed using an SVM classifier. In this work, publicly available databases are modified (such as: DRISHTI‐GS1‐R, ORIGA‐R, RIM‐ONE2‐R, LAG‐R, and ACRIMA‐R) based on oversampling data‐level technique for validating the performance of deep ensemble model. Ensembling the best configurations reports an overall classification accuracy of 93.4%, 79.6%, 91.3%, 99.5%, and 99.6% in DRISHTI‐GS1‐R, ORIGA‐R, RIM‐ONE2‐R, ACRIMA‐R, and LAG‐R databases, respectively. [ABSTRACT FROM AUTHOR]

A polyp is one of the major causes of gastroenterology, which leads to colorectal cancer. The detection of polyps by colonoscopy imaging is a significant challenge because of the diversity in polyp structure and lack of examination accuracy. To solve this problem, the automatic segmentation of polyps can be used to enhance examination accuracy and reduce gastrointestinal (GI) disease. In this paper, the framework of polyp image segmentation is developed by a deep learning approach, especially a convolutional neural network. This proposed framework used the Kvasir‐SEG database, which contains 1000 GI polyp images and corresponding segmentation masks according to annotation by medical experts. This database is divided into 900 for training images and 100 for testing images. This framework is based on image preprocessing and two types of SegNet architecture to obtain the segmented polyp image. This paper has demonstrated state‐of‐the‐art performance on both VGG‐16, and VGG‐19 networks for training and testing data to address colorectal cancer screening rates. The results confirmed that the VGG‐19 model has outperformed the VGG‐16 model via all evaluation parameters except sensitivity for the polyp segmentation on the Kvasir‐SEG dataset. Additionally, it will support gastroenterologists during medical strategy to correctly choose the treatment with less time. [ABSTRACT FROM AUTHOR]

Thyroid is a widespread disease, affecting most victims. The diagnosis of thyroid remains a complex process, as its detection in patients is highly intricate. Hence, the doctors are needed to be aware of the risk factors and symptoms of the disease. This paper aims to propose a novel thyroid diagnosis scheme, involving three major phases: (a) feature extraction, (b) optimal feature selection, and (c) classification. Initially, the thyroid image and the related data serve as input for diagnosing the disease. In the first phase, the features like, gray level co‐occurrence matrix (GLCM), gray level run length matrix (GLRM), local binary pattern (LBP), local vector pattern (LVP), and local tetra patterns (LTrP) are extracted from the input image. Additionally, the features from data are extracted using Principal Component Analysis (PCA) for resolving the issue of "curse of dimensionality." The optimal features are then selected using a hybrid optimization approach. The optimally selected features of the image and the data are then subjected to the classification process via convolutional neural network (CNN) and neural network (NN), respectively. Both the classified outputs undergo "AND" binary operation to yield the final classified output. To yield effective classification, the NN model is trained by tuning its weights using the proposed algorithm. Further, this paper introduces a new hybrid algorithm, termed firefly updated lion optimization (SLnO) algorithm (FU‐SLnO), for attaining optimal outcomes. Finally, the efficiency of the proposed work is compared over few other conventional approaches and its superiority is proven. [ABSTRACT FROM AUTHOR]

Several radiologists have paid attention to computer‐aided detection (CAD) systems which assist in classifying diseases on chest x‐ray (CXR). Recently, with the outbreak of COVID‐19, CAD based on deep learning has an important role in screening COVID‐19 on CXR. However, imbalanced training datasets such as COVID‐19 datasets, COVID‐19 (473), pneumonia (5458), and normal (7966) cause difficulty in classification. In this paper, we suggest a new evaluation approach, OVASO, that selectively combines one‐versus‐all (OVA) classifier and one‐versus‐one (OVO) to overcome class imbalance caused by the lower number of COVID‐19 training datasets. In addition, as part of efforts to properly apply transfer learning, we initialized batch normalization (BN) values including γ and β from the viewpoint of transfer learning and found that appropriate initialization at all binary models, OVASO's components, usually increased the binary models' performance. As a result, the proposed OVASO model achieved improved accuracy and F1‐score of 95.33% and 95.88%, respectively. Furthermore, the suggested OVASO performed similarly to COVID‐Net, which is the current state‐of‐the‐art model for classifying COVID‐19 on CXR. [ABSTRACT FROM AUTHOR]

Cancer grade is an indicator of the aggressiveness of cancer. It is used for prognosis and treatment decisions. Conventionally cancer grading is performed manually by experienced pathologists via microscopic examination of pathology slides. Among the three factors involved in breast cancer grading (mitosis count, nuclear atypia, and tubule formation), mitotic cell counting is the most challenging task for pathologists. It is possible to automate this task by applying computational algorithms on pathology slides images. Lack of sufficiently large datasets and class imbalance between mitotic and non‐mitotic cells in slide images are the two major challenges in developing effective deep learning‐based methods for mitosis detection. In this paper, we propose a new approach and a method based on that to address these challenges. The high training data requirement of the advanced deep neural network is met by combining two datasets from different sources after a color‐normalization process. Class imbalance is addressed by the augmentation of the mitotic samples in a context‐preserving manner. Finally, a customized convolutional neural network classifier is used to classify the candidate cells into the target classes. We have used the publicly available datasets MITOS‐ATYPIA and MITOS for the experiments. Our method outperforms most of the recent methods that are based on independent datasets and at the same time offers adaptability to the combination of datasets from different sources. [ABSTRACT FROM AUTHOR]

The cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately. [ABSTRACT FROM AUTHOR]

Visual interpretation of chest X‐rays (CXRs) is tedious and prone to error. Significant amount of time is spent by the radiologist in differentiating normal from abnormal CXRs and in identifying the location and type of abnormalities. An assistance tool for automatically classifying normal and different types of abnormal CXRs can facilitate the diagnosis and potentially save time costs. In this paper, a novel hybrid model having concatenation of Visual Geometry Group (VGG19) network and Entropy features as a modified deep convolutional neural network (DCNN) architecture, called VEntNet, is proposed for the automated multi‐class categorization of CXR images into normal, coronavirus disease (COVID), tuberculosis (TB), viral pneumonia, and bacterial pneumonia. The VEntNet model implemented consists of deep features extraction from convolutional layers of VGG19 network which are then concatenated with hand‐crafted entropy features extracted from CXRs. The concatenated features are then fed to the fully connected (FC) layers for performing multi‐class categorization using Softmax activation function. The performance of proposed VEntNet model is compared with other DCNNs with and without the hybrid approach for categorization of closely related lung pathologies and normal CXR images. Our proposed VEntNet achieved accuracies of 98.78% and 90.96%, respectively, for four and five‐class classification of CXRs. Thus, it is demonstrated that among the different DCNNs, our VEntNet outperformed in four‐class CXR categorization tasks. The proposed model can potentially save time by facilitating the screening of CXRs to identify those with abnormalities present as well as to categorize the abnormalities. [ABSTRACT FROM AUTHOR]

There are many different types of nuclei in a tumor tissue. We can identify the specific nuclei and their distribution in the tissue to reflect the current cancer state of histopathological images. However, due to the existence of cellular heterogeneity, the recognition of nuclei in histopathological images has always been a problem of computer vision. In the paper, we use the transfer learning of Deep Convolutional Neural Network to classify nuclei, and found that adjusting the size of the nuclear image to a certain size can improve the accuracy of the nuclei classification model, while not significantly reducing the nuclei classification efficiency. Through further research, it was confirmed that the environment around the nuclei can bring great help to the model classification. Based on the principle, we design a feature fusion model. We extract features from nuclei image different sizes by CNN, fuse the features, and then use fully connected layer to classify the features. Experiments have proved that the feature fusion model has a considerable improvement in accuracy compared to the normal classification model. [ABSTRACT FROM AUTHOR]

Lung tumor is a complex disease caused due to the irregular growth of lung cells. A key factor in effective treatment planning is the early detection of lung tumor. Visual similarity between benign and malignant nodules, heterogeneity and low contrast variation are the factors that make accurate cancerous lesion recognition, a very challenging task. In this paper, Optimized Deep convolutional neural network (DCNN) and Fuzzy C‐means with Equilibrium optimizer (FCM‐EO) is proposed for classification and segmentation of CT lung images. The proposed architecture is comprised of four phases such as preprocessing, feature extraction, classification and segmentation. In preprocessing, the weighted mean histogram analysis (WMHA) is utilized to enhance the quality of images and noise removal. Hybrid Dual tree‐complex wavelet transform (DT‐CWT) with Gabor filter is proposed in feature extraction to extract the features from the preprocessed images. DCNN model is designed to classify the original images into benign and malignant images. The weight of DCNN model is updated using the Enhanced black widow optimization algorithm (EBWOA). In segmentation, FCM‐EO is introduced to identify the tumor regions and remove the outliers from the malignant images. LIDC‐IDRI dataset is utilized for the experimental analysis and MATLAB is the implementation tool. The simulation analysis is performed for both the classification and segmentation processes. Accuracy, specificity, sensitivity, precision, F‐measure, FROC, DSC, MCC, and IoU are evaluated for both these processes. The experimental results showed the proposed framework is efficient for the identification of tumor from the CT lung images. [ABSTRACT FROM AUTHOR]

The mortality risk factors for coronavirus disease (COVID‐19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID‐19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic‐based adaptive momentum estimation (GB‐ADAM) algorithm. The GB‐ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8+ T Lymphocyte (Count), D‐dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C‐reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID‐19 signs in CT scans included ground‐glass opacity (GGO), followed by crazy‐paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models. [ABSTRACT FROM AUTHOR]

In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet‐B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: "COVID‐19 Image Dataset," "COVID‐19 Pneumonia Normal Chest X‐ray (PA) Dataset," and "COVID‐19 Radiography Database" for COVID‐19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1‐score values, as well. Contribution of paper is as follows: COVID‐19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer‐aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy. [ABSTRACT FROM AUTHOR]

The symptoms of lung cancer mainly manifest as lung nodules. The recognition and diagnosis of a ground glass nodule (GGN) are relatively difficult, and its image features are not easy to extract. To improve the accuracy of segmentation and invasive classification of GGN, a region adaptive Markov random field (MRF) model and a two‐channel integrated network based on densely connected convolutional neural networks (DenseNet) are developed in this paper. First, the lung parenchyma is segmented coarsely, and the corresponding contour is repaired. Afterwards, the fuzzy C‐means (FCM) clustering algorithm and a simple MRF model for the coarse segmentation of GGN are applied. Then, the region adaptive MRF model for the fine segmentation of GGN is used. Moreover, the two‐channel integrated network based on DenseNet is used for GGN invasive classification. Results show that the average value of the overlapping area ratio of the segmentation result to that obtained by one physician was 0.9144, and the accuracy of the proposed GGN invasive classification model, the specificity, sensitivity, and the area under curve were 92.553%, 87.500%, 97.826%, and 0.9715, respectively. The proposed segmentation method can segment GGN more accurately, and the corresponding GGN invasive classification model achieved satisfying classification performance. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a major medical concern worldwide. Long‐term diabetes can affect the retina of the eye and lead to diabetic retinopathy (DR) and diabetic macular edema (DME). Proper screening and consultation with an ophthalmologist are necessary to prevent avoidable vision loss. As DR and DME have become more prevalent, automated screening is essential to provide cost‐effective and rapid solutions with reduced human resources requirements. This paper aims to provide a comprehensive review of the literature on computer‐aided diagnosis of DR and DME. We identified the studies on automated five‐class grading of DR according to International Clinical Diabetic Retinopathy severity scale and three class grading of diabetic maculopathy, using fundus images. A systematic search on research repositories was conducted, and relevant studies were scrutinized and included in the review. The studies were reported in nearly 100 different journals. We have reviewed the studies in all aspects including datasets, preprocessing, non‐deep learning, and deep learning‐based algorithms, and evaluation metrics. Significant contributions in developing automated tools for DR/DME grading are highlighted. We have identified and discussed research gaps and challenges. This will help researchers to get an updated summary of work done in the area. Deep learning‐based algorithms have outperformed the traditional algorithms in the domain. Despite their promising performance, these algorithms reveal the potential for significant improvements to become a reliable tool in clinical settings. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging, despite of its significant role in today's healthcare, suffers from long image acquisition time which leads to patient discomfort and cost increment. Compressive sensing magnetic resonance imaging, where clinically acceptable images are reconstructed using partially sampled k‐space data, is one possible approach to mitigate this problem. The recent evolution in compressive sensing magnetic resonance imaging field is the model based deep learning approach, which is comprised of cascaded convolutional neural network based denoizer and data consistency layer. In this paper, we propose an edge guided model based deep learning approach employing U‐net module as an artifact removal unit. The proposed model contains cascaded U‐net architectures with interleaved data consistency layer. To effectively retain the fine details in the reconstructed output, along with the image, edge maps of the image were also applied at the input of each stage in the cascaded structure and the edge map loss was incorporated in the objective function along with the pixel loss. Experiments were performed on MR‐PD and MR‐T1 images with different sampling patterns. Qualitative and quantitative comparison of the results obtained with the proposed method with other model based and deep learning methods validates the superiority of the proposed method in reconstructing high quality magnetic resonance images. [ABSTRACT FROM AUTHOR]

Medical image fusion can effectively combine different images with different parts and complements each other, which is a hotspot in image processing. However, the medical image acquisition equipment usually obtains single image, so it is unable to use dataset to train convolutional neural network to extract stable image features. This paper proposes an approach using the Transfer Learning combined with the Limited Broyden, Fletcher, Goldfarb, and Shanno (L‐BFGS) optimization algorithm to achieve single medical image fusion. The Transfer Learning which includes pre‐trained model and parameters can be used as a feature extractor to extract or compress features in medical images. Firstly, different single medical images were input into the trained VGG16 Transfer Learning parameter model to extract image features, and then the maximum features between different images were obtained through feature comparison. Finally, L‐BFGS optimization algorithm is used to approximate the initial image features to the maximum features, to achieve the fusion effect (the initial image can be any image of the same size and depth as the source image). The experimental results show that the algorithm is effective and very suitable for medical image fusion. [ABSTRACT FROM AUTHOR]

Breast cancer has high incidences and mortality rates in women worldwide. Malignancy could be detected manually by experienced pathologists based on Hematoxylin and Eosin (H&E) stained images. However, it is time‐consuming and experience‐dependent, making early diagnosis a big challenge. In this paper, a methodology for breast cancer classification based on histopathological images with deep learning was described. A residual learning‐based convolutional neural network named myResNet‐34 was designed for malignancy‐and‐benign classification. In addition, an algorithm automatically generating the target image for stain normalization was proposed, which eliminated the bias caused by manual selection of the reference image. Elastic distortion was introduced and combined with affine transformation for data augmentation considering the characteristics of the H&E images. Experiments were conducted on BreakHis dataset with the proposed framework. Promising results were achieved with an average classification accuracy of around 91% on image‐level classification. Results indicated that both our data augmentation and stain normalization effectively improved the classification accuracy by 2‐3%. [ABSTRACT FROM AUTHOR]

The application of deep transfer learning techniques has been successful in developing accurate systems for brain tumour classification on large‐scale medical image databases. For small databases, feature learning by deep neural networks is not robust. The systems based on domain‐specific hand‐crafted features have limited accuracy. In this paper, the authors focus on developing accurate models that could be trained effectively using a smaller number of data samples. A siamese neural network (SNN) is designed to extract features from brain magnetic resonance imaging (MRI) images. The SNN is realised using a 3‐layer, fully connected neural network. The designed SNN has lesser complexity and fewer parameters than deep transfer‐learned convolutional neural networks (CNN). A nearest neighbourhood analysis, using Euclidean and Mahalanobis distances, is conducted on the SNN encoded feature space. The encoded feature space is two dimensional, such that the neighbourhood analysis is computationally less intensive. For the neighbourhood analysis, a k‐nearest neighbour (k‐NN) model is utilised. The proposed method is evaluated using three publicly available datasets, namely, Radiopaedia, Harvard and Figshare repositories. The respective classification accuracy on cross‐validation is 92.6%, 98.5% and 92.6%. Other metrics used for the performance evaluation include F‐score, Specificity and balanced accuracy. The underlying network architecture and the design choice of network layers allow the implementation of the SNN in environments with low computational resources. The SNN features are found to be more effective than the hand‐designed features, and the deep transfer learned features for the stated problem. [ABSTRACT FROM AUTHOR]

Recently, image processing schemes are widely used to improve disease detection performance in many medicinal fields. Cancer is considered as one of the most deadly disease and early diagnosis of cancer is the complicated task in the field of medicine. In this paper, we present the two pretrained convolutional neural network (CNN) based on ensemble models such as VGG19 and VGG16 for cancer diagnosis that classifies both normal and abnormal images. The dilemma associated with CNN hyperparameter tuning complicates while diagnosing cancer. Hence, we propose multistrategy based artificial electric field (M‐AEF) algorithm for hyper‐parameter tuning in CNN thereby finding the optimal values. The exponentially decaying learning rates are more helpful to train CNN and prevent it from a local minimum. Thus, random minority over‐sampling and random majority under‐sampling address the imbalanced issue present in the dataset. The images are obtained from three different datasets namely the Kaggle dataset, International Collaboration on Cancer Reporting (ICCR) dataset, and cancer programming dataset for cancer detection. The experimental results are executed in MATLAB software and various performance analyses are carried out. Finally, the proposed method demonstrated better and higher cancer detection performance than other methods. [ABSTRACT FROM AUTHOR]

Novel corona virus COVID‐19 has spread rapidly all over the world. Due to increasing COVID‐19 cases, there is a dearth of testing kits. Therefore, there is a severe need for an automatic recognition system as a solution to reduce the spreading of the COVID‐19 virus. This work offers a decision support system based on the X‐ray image to diagnose the presence of the COVID‐19 virus. A deep learning‐based computer‐aided decision support system will be capable to differentiate between COVID‐19 and pneumonia. Recently, convolutional neural network (CNN) is designed for the diagnosis of COVID‐19 patients through chest radiography (or chest X‐ray, CXR) images. However, due to the usage of CNN, there are some limitations with these decision support systems. These systems suffer with the problem of view‐invariance and loss of information due to down‐sampling. In this paper, the capsule network (CapsNet)‐based system named visual geometry group capsule network (VGG‐CapsNet) for the diagnosis of COVID‐19 is proposed. Due to the usage of capsule network (CapsNet), the authors have succeeded in removing the drawbacks found in the CNN‐based decision support system for the detection of COVID‐19. Through simulation results, it is found that VGG‐CapsNet has performed better than the CNN‐CapsNet model for the diagnosis of COVID‐19. The proposed VGG‐CapsNet‐based system has shown 97% accuracy for COVID‐19 versus non‐COVID‐19 classification, and 92% accuracy for COVID‐19 versus normal versus viral pneumonia classification. Proposed VGG‐CapsNet‐based system available at https://github.com/shamiktiwari/COVID19%5fXray can be used to detect the existence of COVID‐19 virus in the human body through chest radiographic images. [ABSTRACT FROM AUTHOR]

Among Coronavirus, as with many other viruses, receptor interactions are an essential determinant of species specificity, virulence, and pathogenesis. The pathogenesis of the COVID‐19 depends on the virus's ability to attach to and enter into a suitable human host cell. This paper presents a cockroach optimized deep neural network to detect COVID‐19 and differentiate between COVID‐19 and influenza types A, B, and C. The deep network architecture is inspired using a cockroach optimization algorithm to optimize the deep neural network hyper‐parameters. COVID‐19 sequences are obtained from repository 2019 Novel Coronavirus Resource, and influenza A, B, and C sub‐dataset are obtained from other repositories. Five hundred ninety‐four unique genomes sequences are used in the training and testing process with 99% overall accuracy for the classification model. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy is a retinal abnormality which is characterized by progressive damage to the retina, eventually leading to irreversible blindness. In this paper, we propose an aggregated residual transformation‐based model for automatic multistage classification of diabetic retinopathy. The proposed model obtains 99.68% overall classification accuracy, 99.68% sensitivity, 99.89% specificity and 99.68% precision without overfitting on the MESSIDOR dataset. Further, the model obtains an accuracy of 99.89% for stage 0, 99.89% for stage 1, 99.68% for stage 2 and 99.89% for stage 3 of diabetic retinopathy. In comparison to residual network, the model shows an overall accuracy gain of 0.52%. The model also ensures an overall improvement of more than 6% in accuracy, 1.2% in sensitivity and 2.43 % in specificity when compared to best results reported in the literature. The proposed work outperforms the existing methods and achieves state‐of‐the‐art results for the multistage classification of diabetic retinopathy. [ABSTRACT FROM AUTHOR]

This paper aimed to evaluate the effect of various electrode configurations on applying a convolutional neural network (CNN) to recognize uterine contraction (UC) with Electrohysterogram (EHG) signals. Seven 8‐electrode configurations and thirteen 4‐electrode configurations were selected from the 4 × 4 electrode grid in the Icelandic 16‐electrode EHG database. EHG signals were divided into UC and non‐UC sections of 45 seconds and saved as images. Each 8‐electrode configuration with 7152 images and 4‐electrode configuration with 3576 images were applied to train CNN to recognize UCs. A scoring method was proposed based on the area under the curve (AUC) and the accuracy to evaluate the effect of electrode configurations on recognizing UCs. The EHG signals from the 4 electrodes on the upper left of the uterus showed the best classification performance (AUC = 0.79, Accuracy = 0.72, Score = 2.30). [ABSTRACT FROM AUTHOR]

Early detection and proper screening are essential to prevent vision loss due to glaucoma. In recent years, convolutional neural network (CNN) has been successfully applied to the color fundus images for the automatic detection of glaucoma. Compared to the existing automatic screening methods, CNNs have the ability to extract the distinct features directly from the fundus images. In this paper, a deep learning architecture based on a CNN is designed for the classification of glaucomatous and normal fundus images. An 18–layer CNN is designed and trained to extract the discriminative features from the fundus image. It comprises of four convolutional layers, two max pooling layers, and one fully connected layer. A two–stage tuning approach is proposed for the selection of suitable batch size and initial learning rate. The proposed network is tested on DRISHTI–GS1, ORIGA, RIM–ONE2 (release 2), ACRIMA, and large–scale attention–based glaucoma (LAG) databases. Rotation data augmentation technique is employed to enlarge the dataset. Randomly selected 70% of images are used for training the model and remaining 30% images are used for testing. An overall accuracy of 86.62%, 85.97%, 78.32%, 94.43%, and 96.64% are obtained on DRISHTI–GS1, RIM–ONE2, ORIGA, LAG, and ACRIMA databases, respectively. The proposed method has achieved an accuracy, sensitivity, specificity, and precision of 96.64%, 96.07%, 97.39%, and 97.74%, respectively, for ACRIMA database. Compared to other existing architectures, the proposed method is robust to Gaussian noise and salt–and–pepper noise. [ABSTRACT FROM AUTHOR]

The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content‐based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet‐18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet‐18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state‐of‐art conventional methods. Furthermore, we constructed a Gastric‐Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis. [ABSTRACT FROM AUTHOR]

Biopsy is one of the most commonly used modality to identify breast cancer in women, where tissue is removed and studied by the pathologist under the microscope to look for abnormalities in tissue. This technique can be time‐consuming, error‐prone, and provides variable results depending on the expertise level of the pathologist. An automated and efficient approach not only aids in the diagnosis of breast cancer but also reduces human effort. In this paper, we develop an automated approach for the diagnosis of breast cancer tumors using histopathological images. In the proposed approach, we design a residual learning‐based 152‐layered convolutional neural network, named as ResHist for breast cancer histopathological image classification. ResHist model learns rich and discriminative features from the histopathological images and classifies histopathological images into benign and malignant classes. In addition, to enhance the performance of the developed model, we design a data augmentation technique, which is based on stain normalization, image patches generation, and affine transformation. The performance of the proposed approach is evaluated on publicly available BreaKHis dataset. The proposed ResHist model achieves an accuracy of 84.34% and an F1‐score of 90.49% for the classification of histopathological images. Also, this approach achieves an accuracy of 92.52% and F1‐score of 93.45% when data augmentation is employed. The proposed approach outperforms the existing methodologies in the classification of benign and malignant histopathological images. Furthermore, our experimental results demonstrate the superiority of our approach over the pre‐trained networks, namely AlexNet, VGG16, VGG19, GoogleNet, Inception‐v3, ResNet50, and ResNet152 for the classification of histopathological images. [ABSTRACT FROM AUTHOR]

Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer‐aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state‐of‐the‐art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy. [ABSTRACT FROM AUTHOR]