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Automatic segmentation of various hepatic structures from computed tomography images is of great importance in surgery and treatment planning for patients diagnosed with liver cancer. This work tends to identify the optimal semantic segmentation architecture for the task of segmentation of hepatic structures from computed tomography (CT) images. Segmentation of hepatic structures from CT images is carried out using four popular semantic segmentation architectures viz. fully convolutional network (FCN), SegNet, U-Net and DeepLabV3 + with various encoder configurations on 3D-ircadb-01, CHAOS and on an institutional dataset images. Segmentation of liver, liver tumour, hepatic artery, portal vein and hepatic vein are carried out simultaneously using a single network as a dense pixel prediction task. An extensive experimental assessment of hepatic structures segmentation was carried out using 13 networks derived from the aforesaid 4 segmentation architectures. The segmentation performance is assessed in terms of segmentation accuracy, dice coefficient, Jaccard index and boundary F1score. Experimental results show that DeepLabV3 + architecture with Xception encoder records better segmentation performance with dice coefficient of 98.13% 98.87% and 98.92% on 3D-ircadb-01, CHAOS and on the institutional datasets respectively. The comparison with the state-of-the-art techniques presented exemplifies the effectiveness of the fully depth separable convolutional network on the segmentation of various hepatic structures. Depth separable convolution applied to atrous spatial pyramid pooling (ASPP) with multiple sampling rates at the decoder of the DeepLabV3 + architecture together with the entirely separable convolution based Xception model as encoder stands as evidence towards the exceptional segmentation performance of hepatic structures. [ABSTRACT FROM AUTHOR]

Introduction: This study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors. Methods: The MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD). Results: When applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth. Discussion: FusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance. [ABSTRACT FROM AUTHOR]

Lung cancer is known to be one of the leading causes of death worldwide. There is a chance of increasing the survival rate of the patients if detected at an early stage. Computed Tomography (CT) scans are prominently used to detect and classify lung cancer nodules/tumors in the thoracic region. There is a need to develop an efficient and reliable computer-aided diagnosis model to detect lung cancer nodules accurately from CT scans. This work proposes a novel kernel-based active-contour (KAC) SegNet deep learning model to perform lung cancer nodule detection from CT scans. The active contour uses a snake method to detect internal and external boundaries of the curves, which is used to extract the Region Of Interest (ROI) from the CT scan. From the extracted ROI, the nodules are further classified into benign and malignant using a Dense AlexNet deep learning model. The key contributions of this work are the fusion of an edge detection method with a deep learning segmentation method which provides enhanced lung nodule segmentation performance, and an ensemble of state-of-the-art deep learning classifiers, which encashes the advantages of both DenseNet and AlexNet to learn better discriminative information from the detected lung nodules. The experimental outcome shows that the proposed segmentation approach achieves a Dice Score Coefficient of 97.8% and an Intersection-over-Union of 92.96%. The classification performance resulted in an accuracy of 95.65%, a False Positive Rate, and False Negative Rate values of 0.0572 and 0.0289. The proposed model is robust compared to the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a harmful eye state, which influences diabetic patients. Unless earlier detected, it affects the retinal portion and ultimately causes eternal vision loss. An earlier diagnosis of DR is more vital for treatment purposes. Though, DR diagnosing is a highly complicated process, which needs a knowledgeable ophthalmologist. In this work, Deep Residual Network-Remora Tuna Swarm Optimization (DRN-RTSO) is introduced for DR classification. A fundus image considered is pre-processed utilizing an adaptive wiener filter. Then, lesions are segmented employing SegNet includes Microaneurysms, Haemorrhages, Soft Exudates and Hard Exudates. Thereafter, blood vessel segmentation is conducted on the pre-processed image using Dense U-Net. Afterwards, feature extraction is carried out considering input fundus image and segmented outputs. At last, DR is classified into normal, proliferative, mild non-proliferative (NPDR), severe NPDR and moderate NPDR utilizing DRN that is tuned utilizing RTSO. The RTSO is devised by incorporating Remora Optimization Algorithm (ROA) with Tuna Swarm Optimization (TSO). In addition, DRN-RTSO attained maximal accuracy of 91.5%, negative predictive value (NPV) of 93.3%, positive predictive value (PPV) of 91.1%, true negative rate (TNR) of 91.7% and true positive rate (TPR) of 92.3%. [ABSTRACT FROM AUTHOR]

Recent advances in computer vision and deep learning algorithms have yielded intriguing results. It can perform tasks previously requiring human eyes and brains. Semantic video segmentation for autonomous cars is difficult due to the high cost, low latency, and performance requirements of convolutional neural networks (CNNs). Deep learning architectures like SegNet and FlowNet 2.0 on the Cambridge-driving labeled video database (CamVid) dataset enable low-latency pixel-wise semantic segmentation of video features. Because it uses SegNet and FlowNet topologies, it is ideal for practical applications. The decision network chooses an optical flow or segmentation network for an image frame based on the expected confidence score. Combining this decision-making method with adaptive scheduling of the key frame approach can speed up the process. ResNet50 SegNet has a “54.27%” mean intersection over union (MIoU) and a “19.57” average FPS. In addition to decision network and adaptive key frame sequencing, FlowNet2.0 increased graphics processing unit (GPU) frame processing per second to “30.19” with a MIoU of “47.65%”. The GPU is used “47.65%” of the time. This performance gain illustrates that the video semantic segmentation network is faster without sacrificing quality. [ABSTRACT FROM AUTHOR]

Precision agriculture uses data gathered from various sources to improve agricultural yields and the effectiveness of crop management techniques like fertiliser application, irrigation management, and pesticide application. Reduced usage of agrochemicals is a key step towards more sustainable agriculture. Weed management robots which can perform tasks like selective sprinkling or mechanical weed elimination, contribute to this objective. A trustworthy crop/weed classification system that can accurately recognise and classify crops and weeds is required for these robots to function. In this paper, we explore various deep learning models for achieving reliable segmentation results in less training time. We classify every pixel of the images into different categories using semantic segmentation models. The models are based on an encoder-decoder architecture, where feature maps are extracted during encoding and spatial information is recovered during decoding. We examine the segmentation output on a beans dataset containing different weeds, which were collected under highly distinct environmental conditions, including cloudy, rainy, dawn, evening, full sun, and shadow. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is now one of the most common types of cancer in gents and one of the most important causes of death in the United States. The best non-invasive imaging technique for diagnosing PCa is Magnetic Resonance Image. Each step in the conventional methods is optimized without allowing for error tolerance, resulting in significant computational costs. Hence, a Developed LHFGSO-DMN for PCa detection is introduced to reduce the proportion of PCa deaths. Modifying the objective function with dice coefficient and pixel-wise cross-entropy creates the optimized multi-objective semantic segmentation model known as multi-objective SegNet. This model is trained using the proposed Light Henry Firefly Gas Solubility Optimization (LHFGSO). The LHFGSO is the integration of a Henry Firefly Gas Solubility Optimization (HFGSO) and Light Spectrum Optimizer, and HFGSO is the formation of Henry Gas Solubility Optimization and Firefly Algorithm. Additionally, the LHFGSO method is used to train the Deep Maxout Network (DMN), which is used for cancer detection. The introduced multi-objective SegNet model with DMN for PCa growth recognition strategy reached superior execution measures with an accuracy of 94.63%, sensitivity of 93.46% and specificity of 95.72%. [ABSTRACT FROM AUTHOR]

We present a novel deep learning approach to a unique image processing application: high-speed (>1000 fps) video footage of lightning. High-speed cameras enable us to observe lightning with microsecond resolution, characterizing key processes previously analyzed manually. We evaluate different semantic segmentation networks (DeepLab3+, SegNet, FCN8s, U-Net, and AlexNet) and provide a detailed explanation of the image processing methods for this unique imagery. Our system architecture includes an input image processing stage, a segmentation network stage, and a sequence classification stage. The ground-truth data consists of high-speed videos of lightning filmed in South Africa, totaling 48,381 labeled frames. DeepLab3+ performed the best (93–95% accuracy), followed by SegNet (92–95% accuracy) and FCN8s (89–90% accuracy). AlexNet and U-Net achieved below 80% accuracy. Full sequence classification was 48.1% and stroke classification was 74.1%, due to the linear dependence on the segmentation. We recommend utilizing exposure metadata to improve noise misclassifications and extending CNNs to use tapped gates with temporal memory. This work introduces a novel deep learning application to lightning imagery and is one of the first studies on high-speed video footage using deep learning. [ABSTRACT FROM AUTHOR]

Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC-2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models. [ABSTRACT FROM AUTHOR]

In recent times, microplastics (MPs) have emerged as notable contaminants within several environments, especially in water bodies. The characterization and description of MPs necessitate extensive and laborious analytical methods, making this part of MPs research an essential issue. In this research, SegNet‐Vision Outlooker (VOLO), a computer vision and deep learning (DL)‐based model, is proposed for detecting and classifying MPs present in a water environment. This research model includes step‐by‐step processes such as data collection, preprocessing, filtering and enhancement, augmentation, segmentation, feature extraction, and classification for detecting MPs. The key objective of this research model is to improve the classification accuracy in detecting MPs and to validate the model's effectiveness in handling holographic images. The Holographic Image MPs dataset is collected and used to evaluate the model. In preprocessing, image rescaling is performed to match the proposed model's input resolution as 224 × 224. After rescaling, the images are applied to remove noise using a bilateral filtering technique. The contrast‐limited adaptive histogram equalization (CLAHE) method is applied to enhance the image with better contrast and brightness, which helps the model to segment and classify the images accurately. The enhanced images are applied to the SegNet model for segmentation, which segmented the images according to the MP classes. Based on the segmented images, the VOLO‐D1 model extracted the features and classified the images to detect the MPs present in the images. The SegNet‐VOLO model attained 97.70% detection rate, 98.26% accuracy, 98.13% F1‐score, and 98.62% precision. These performances are compared with the various existing models discussed in the review, where the research model outperformed all the models with better performances. [ABSTRACT FROM AUTHOR]

IntroductionThis study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors.MethodsThe MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD).ResultsWhen applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth.DiscussionFusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance.

BACKGROUND: Thyroid tumor is considered to be a very rare form of cancer. But recent researches and surveys highlight the fact that it is becoming prevalent these days because of various factors. OBJECTIVES: This paper proposes a novel hybrid classification system that is able to identify and classify the above said four different types of thyroid tumors using high end artificial intelligence techniques. The input data set is obtained from Digital Database of Thyroid Ultrasound Images through Kaggle repository and augmented for achieving a better classification performance using data warping mechanisms like flipping, rotation, cropping, scaling, and shifting. METHODS: The input data after augmentation goes through preprocessing with the help of bilateral filter and is contrast enhanced using dynamic histogram equalization. The ultrasound images are then segmented using SegNet algorithm of convolutional neural network. The features needed for thyroid tumor classification are obtained from two different algorithms called CapsuleNet and EfficientNetB2 and both the features are fused together. This process of feature fusion is carried out to heighten the accuracy of classification. RESULTS: A Multilayer Perceptron Classifier is used for classification and Bonobo optimizer is employed for optimizing the results produced. The classification performance of the proposed model is weighted using metrics like accuracy, sensitivity, specificity, F1-score, and Matthew's correlation coefficient. CONCLUSION: It can be observed from the results that the proposed multilayer perceptron based thyroid tumor type classification system works in an efficient manner than the existing classifiers like CANFES, Spatial Fuzzy C means, Deep Belief Networks, Thynet and Generative adversarial network and Long Short-Term memory. [ABSTRACT FROM AUTHOR]

Currently, one amongst most primary health problems and an enormously transmittable disease is Tuberculosis (TB). This disease spreads all over the world and is commonly developed by Mycobacterium TB (MTB). TB causes fatality if it is not identified at earlier stages. Thus, accurate and effectual model is necessary for detecting infection level of TB. Here, Xception Taylor Cascade Neuro Network (Xception T-Cascade NNet) is presented for infection level identification of TB utilizing sputum images. Firstly, input sputum image acquired from certain database is pre-processed by denoising and histogram equalization utilizing contrast limited adaptive histogram equalization (CLAHE). SegNet is utilized for bacilli segmentation and it is tuned by White Shark Optimizer (WSO). Thereafter, suitable features such as designed discrete cosine transform (DCT) with angled local directional pattern (ALDP), statistical features, shape features and gray-level co-occurrence model (GLCM) texture features are extracted for further processing. Lastly, infection level identification of TB is conducted by Xception T-Cascade NNet. However, Xception T-Cascade NNet is an integration of Xception with Cascade Neuro-Fuzzy Network (NFN) by Taylor concept. In addition, Xception T-Cascade NNet achieved 88.5% of accuracy, 90.8% of true negative rate (TNR) and 89.4% of true positive rate (TPR) and as well as minimal false negative rate (FNR) of 0.092 and false positive rate (FPR) of 0.106. [ABSTRACT FROM AUTHOR]

Forest roads provide access to remote wooded areas, serving as key transportation routes and contributing to human impact on the local environment. However, large animals, such as bears (Ursus sp.), moose (Alces alces), and caribou (Rangifer tarandus caribou), are affected by their presence. Many publicly available road layers are outdated or inaccurate, making the assessment of landscape objectives difficult. To address these gaps in road location data, we employ CubeSat Imagery from the Planet constellation to predict the occurrence of road probabilities using a SegNet Convolutional Neural Network. Our research examines the potential of a pre-trained neural network (VGG-16 trained on ImageNet) transferred to the remote sensing domain. The classification is refined through post-processing, which considers spatial misalignment and road width variability. On a withheld test subset, we achieve an overall accuracy of 99.1%, a precision of 76.1%, and a recall of 91.2% (F1-Score: 83.0%) after considering these effects. We investigate the performance with respect to canopy coverage using a spectral greenness index, topography (slope and aspect), and land cover metrics. Results found that predictions are best in flat areas, with low to medium canopy coverage, and in the forest (coniferous and deciduous) land cover classes. The results are vectorized into a drivable road network, allowing for vector-based routing and coverage analyses. Our approach digitized 14,359 km of roads in a 23,500 km2 area in British Columbia, Canada. Compared to a governmental dataset, our method missed 10,869 km but detected an additional 5774 km of roads connected to the network. Finally, we use the detected road locations to investigate road age by accessing an archive of Landsat data, allowing spatiotemporal modelling of road access to remote areas. This provides important information on the development of the road network over time and the calculation of impacts, such as cumulative effects on wildlife. [ABSTRACT FROM AUTHOR]

The identification of bacterial colonies is deemed to be crucial in microbiology as it helps in identifying specific categories of bacteria. The careful examination of colony morphology plays a crucial role in microbiology laboratories for the identification of microorganisms. Quantifying bacterial colonies on culture plates is a necessary task in Clinical Microbiology Laboratories, but it can be time‐consuming and susceptible to inaccuracies. Therefore, there is a need to develop an automated system that is both dependable and cost‐effective. Advancements in Deep Learning have played a crucial role in improving processes by providing maximum accuracy with a negligible amount of error. This research proposes an automated technique to extract the bacterial colonies using SegNet, a semantic segmentation network. The segmented colonies are then counted with the assistance of blob counter to accomplish the activity of colony counting. Furthermore, to ameliorate the proficiency of the segmentation network, the network weights are optimized using a swarm optimizer. The proposed methodology is both cost‐effective and time‐efficient, while also providing better accuracy and precise colony counts, ensuring the elimination of human errors involved in traditional colony counting techniques. The investigative assessments were carried out on three distinct sets of data: Microorganism, DIBaS, and tailored datasets. The results obtained from these assessments revealed that the suggested framework attained an accuracy rate of 88.32%, surpassing other conventional methodologies with the utilization of an optimizer. [ABSTRACT FROM AUTHOR]

A growth of the uncontrollable cells in a body causes cancer, which is one among deadly diseases. It can be developed at any parts of human body that may comprise of numerous cellules. The most frequently occurring cancer type is referred to skin cancer that develops in uppermost layer of skin. Skin cancer is a hazardous type of cancer, which affects millions of peoples each year. Detection of skin cancer in earlier phases is challenging and highly expensive process. Thus, DeepSegNet + Fuzzy cognitive map (FCM) is devised newly for skin cancer segmentation and detection. Here, pre-processing of an input skin cancer image is carried out by adaptive bilateral filtering. The segmentation of skin cancer image is performed by DeepSegNet. However, DeepSegNet is formed by incorporating DeepJoint segmentation and SegNet. After that, image augmentation is conducted with segmented image, wherein few augmentation techniques are utilized. Then, features are extracted from augmented image, which includes texture features, statistical features, Local Neighborhood Difference Pattern (LNDP) and also, new feature termed discrete wavelet transform (DWT) based Local Directional Pattern (LDP). Finally, detection of skin cancer is done utilizing FCM, which showed perfect detection output in terms of skin cancer. Furthermore, DeepSegNet achieved maximal dice coefficient value of 0.920 whereas DeepSegNet + FCM attained maximal values of accuracy as 93.3%, NPV as 89.6%, PPV as 91.5%, sensitivity as 94% and specificity as 92.6%. [ABSTRACT FROM AUTHOR]

Accurate and appropriate prostate cancer detection can significantly reduce the death rate. In this research, the Henry Firefly Gas Solubility Optimization (HFGSO)-based Deep Residual Network (DRN) is established for the autonomous detection of prostate cancer. The pre-processing is done by Cuckoo Search-Based (T2FCS) filter and Type 2 Fuzzy. Subsequently, segmentation is exhibited by devised multi-objective SegNet scheme. The multi-objective SegNet method is newly designed by updating the objective function of SegNet with loss function. The multi-objective SegNet is trained by HFGSO. Then, data augmentation is done with cropping and rotation, which improves the performance of detection. At last, cancer identification is executed with DRN, and it is trained by HFGSO. The developed optimized multi-objective SegNet with DRN technique also achieved increased performance for the detection of cancer, with a sensitivity, specificity, and accuracy 0.9367, 0.9130, and 0.9263. [ABSTRACT FROM AUTHOR]

In this research, a novel metaheuristic-based approach for enhancing the image quality of Magnetic resonance imaging (MRI) scans for Alzheimer's disease (AD) classification and segmentation is proposed. The proposed approach involves four phases: data collection, preprocessing, segmentation, and classification. The dataset used in this study is collected from an open-source internet platform and contains four classes of AD. To improve the quality of the collected images, various enhancement techniques are employed, such as local entropy-weighted histogram equalization using an adaptive wind-driven optimization algorithm and guided filter-based image denoising using an adaptive Salp Swarm Optimization algorithm. In addition to this, geometric transformations, image augmentation, noise reduction, and grayscale conversion are also utilized. The proposed segmentation model employs a drop-block in Segmentation Network (SegNet) for regularized feature learning, resulting in highly efficient pixel-wise segmentation. A modified loss function is also introduced for fine-tuning the performance of SegNet. The classification layer of SegNet utilizes a Restricted boltzmann machine (RBM) model for classifying AD. Performance evaluation of the proposed approach is done by considering performance metrics such as accuracy, precision, recall, F1-measure, Root means square error (RMSE), and mean absolute error (MAE). The proposed approach outperforms existing techniques such as Convolutional neural network (CNN), Recurrent neural network (RNN), Long short-term memory (LSTM), Understanding networks (UNET), and SegNet, demonstrating the effectiveness of the proposed metaheuristic-based image quality enhancement approach. The implementation of the proposed approach is carried out using Matlab. [ABSTRACT FROM AUTHOR]

Proper surveillance and maintenance of photovoltaic (PV) systems are crucial to ensure continuous power generation and prevent operational downtimes. However, manual analysis of electroluminescence (EL) images is subjective, time-intensive, and requires significant expertise. To address this issue, a comprehensive deep learning architecture has been developed for the semantic segmentation of 29 different features and defects within EL images of PV panels. The SegNet architecture encoder has been replaced with the VGG16 encoder, which incorporates pre-trained weights to leverage transfer learning during the feature extraction stage. A Convolutional Block Attention Module (CBAM) block has also been introduced to enhance the decoder’s ability to generate fine-grained segmentations. Additionally, the suggested architecture has been evaluated through the application of three different loss functions: weighted categorical cross-entropy loss, categorical cross-entropy, and focal loss. The Attention-Based SegNet architecture proposed with a weighted categorical cross-entropy loss exhibits superior performance in terms of accuracy, F1 score, intersection over union (IoU), precision, recall, mean IoU (mIoU), specificity, Jaccard index, and Dice coefficient. It achieves a Dice coefficient of 0.9408 and an mIoU of 0.9101, outperforming the state-of-the-art SEiPV-Net trained on the same dataset by 8.77% and 4.97%, respectively.

Leaf mining, rust, bacterial blight, and berry pathology are major diseases in coffee plants. These diseases not only reduce yield but also affect quality. Early detection and targeted treatment are crucial to mitigate their effects. This paper introduces an efficient hybrid deep learning model, SUNet, for prediction and classification of healthy and diseased coffee leaves. SUNet integrates U-Net with Segnet’s encoding system, using VGG16 for robust feature extraction. A decoder with skip connections is used to preserve spatial details. Mask R-CNN is also employed for instance segmentation, accurately localizing disease spots. A pyramid pooling module captures multi-scale contextual information. The model is tested using two benchmark datasets, JMuBEN and JMuBEN2. These datasets contain a wide range of coffee leaves affected by phoma, cercospora, or rust, along with healthy samples. SUNet achieved significant performance improvement over other models in terms of accuracy, Intersection over Union (IoU), F1-score, precision, and recall by 1.22%, 1.21%, 1.17%, 1.19%, and 1.24%, respectively. These improvements demonstrate that SUNet can be used for the early detection and classification of coffee leaf diseases. Therefore, with precise and timely interventions, SUNet can help farmers minimize crop losses, enhance coffee production quality, and reduce reliance on harmful chemical treatments.

Ultrasound imaging has been used to investigate compression of the median nerve in carpal tunnel syndrome patients. Ultrasound imaging and the extraction of median nerve parameters from ultrasound images are crucial and are usually performed manually by experts. The manual annotation of ultrasound images relies on experience, and intra- and interrater reliability may vary among studies. In this study, two types of convolutional neural networks (CNNs), U-Net and SegNet, were used to extract the median nerve morphology. To the best of our knowledge, the application of these methods to ultrasound imaging of the median nerve has not yet been investigated. Spearman's correlation and Bland–Altman analyses were performed to investigate the correlation and agreement between manual annotation and CNN estimation, namely, the cross-sectional area, circumference, and diameter of the median nerve. The results showed that the intersection over union (IoU) of U-Net (0.717) was greater than that of SegNet (0.625). A few images in SegNet had an IoU below 0.6, decreasing the average IoU. In both models, the IoU decreased when the median nerve was elongated longitudinally with a blurred outline. The Bland–Altman analysis revealed that, in general, both the U-Net- and SegNet-estimated measurements showed 95% limits of agreement with manual annotation. These results show that these CNN models are promising tools for median nerve ultrasound imaging analysis. [ABSTRACT FROM AUTHOR]

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In the human body, white blood cells (WBCs) are crucial immune cells that help in the early detection of a variety of illnesses. Determination of the number of WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, as well as AIDS and leukemia. However, the conventional method of classifying and counting WBCs is time-consuming, laborious, and potentially erroneous. Therefore, this paper presents a computer-assisted automated method for recognizing and detecting WBC categories from blood images. Initially, the blood cell image is preprocessed and then segmented using an effective deep learning architecture called SegNet. Then, the important features are devised and extracted using the EfficientNet architecture. Finally, the WBCs are categorized into four different types using the XGBoost classifier: neutrophils, eosinophils, monocytes, and lymphocytes. The advantages of SegNet, EfficientNet, and XGBoost make the proposed model more robust and achieve a more efficient classification of the WBCs. The BCCD dataset is used to evaluate the performance of the proposed methodology, and the findings are compared to existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, and F1-score. Evaluation results show that the proposed approach has a higher rank-1 accuracy of 99.02% and outperformed other existing techniques.

Image-based gauging stations offer the potential for substantial enhancement in the monitoring networks of river water levels. Nonetheless, the majority of camera gauges fall short in delivering reliable and precise measurements because of the fluctuating appearance of water in the rivers over the course of the year. In this study, we introduce a method for measuring water levels in rivers using both the traditional continuous image subtraction (CIS) approach and a SegNet neural network based on deep learning computer vision. The historical images collected from on-site investigations were employed to train three neural networks (SegNet, U-Net, and FCN) in order to evaluate their effectiveness, overall performance, and reliability. The research findings demonstrated that the SegNet neural network outperformed the CIS method in accurately measuring water levels. The root mean square error (RMSE) between the water level measurements obtained by the SegNet neural network and the gauge station's readings ranged from 0.013 m to 0.066 m, with a high correlation coefficient of 0.998. Furthermore, the study revealed that the performance of the SegNet neural network in analyzing water levels in rivers improved with the inclusion of a larger number of images, diverse image categories, and higher image resolutions in the training dataset. These promising results emphasize the potential of deep learning computer vision technology, particularly the SegNet neural network, to enhance water level measurement in rivers. Notably, the quality and diversity of the training dataset play a crucial role in optimizing the network's performance. Overall, the application of this advanced technology holds great promise for advancing water level monitoring and management in river systems.

Recently, the plantation of sugarcane is done in mechanized manner with billets that are short segments of sugarcane harvested. The automated harvesting procedure can destroy billets and reduces quality of billets. Deep convolution neural network is applied to diagnose sugarcane billet damage (DCNN). A novel technique, namely Aquila Sailfish Optimizer (ASO) algorithm is devised for weight update of neurons in training the DCNN and enhances the efficiency of DCNN. The ASO is obtained by incorporating Aquila Optimizer (AO) and Sailfish Optimizer (SFO). The classification of sugarcane billet damage is done by Chronological Aquila Sailfish Optimizer (CASO) algorithm trained Deep Quantum Neural Network (DQNN), which is trained with obtained by incorporating Chronological concept in ASO. Here, the sugarcane billet damage will be classified into five types, including Crushed (blue), Cracked (red), No buds (yellow), Two buds (green) and Single damaged bud (orange). The developed CASO-based DQNN presented highest precision of 91%, recall of 93.3%, F-measure of 92.1%, and accuracy of 91.5%. [ABSTRACT FROM AUTHOR]

Ultrasound features related to thyroid lesions structure, shape, volume, and margins are considered to determine cancer risk. Automatic segmentation of the thyroid lesion would allow the sonographic features to be estimated. On the basis of clinical ultrasonography B-mode scans, a multi-output CNN-based semantic segmentation is used to separate thyroid nodules' cystic & solid components. Semantic segmentation is an automatic technique that labels the ultrasound (US) pixels with an appropriate class or pixel category, i.e., belongs to a lesion or background. In the present study, encoder-decoder-based semantic segmentation models i.e. SegNet using VGG16, UNet, and Hybrid-UNet implemented for segmentation of thyroid US images. For this work, 820 thyroid US images are collected from the DDTI and ultrasoundcases.info (USC) datasets. These segmentation models were trained using a transfer learning approach with original and despeckled thyroid US images. The performance of segmentation models is evaluated by analyzing the overlap region between the true contour lesion marked by the radiologist and the lesion retrieved by the segmentation model. The mean intersection of union (mIoU), mean dice coefficient (mDC) metrics, TPR, TNR, FPR, and FNR metrics are used to measure performance. Based on the exhaustive experiments and performance evaluation parameters it is observed that the proposed Hybrid-UNet segmentation model segments thyroid nodules and cystic components effectively. [ABSTRACT FROM AUTHOR]

Encoder-decoder-based semantic segmentation models classify image pixels into the corresponding class, such as the ROI (region of interest) or background. In the present study, simple / dilated convolution / series / directed acyclic graph (DAG)-based encoder-decoder semantic segmentation models have been implemented, i.e., SegNet (VGG16), SegNet (VGG19), U-Net, mobileNetv2, ResNet18, ResNet50, Xception and Inception networks for the segment TTUS(Thyroid Tumor Ultrasound) images. Transfer learning has been used to train these segmentation networks using original and despeckled TTUS images. The performance of the networks has been calculated using mIoU and mDC metrics. Based on the exhaustive experiments, it has been observed that ResNet50-based segmentation model obtained the best results objectively with values 0.87 for mIoU, 0.94 for mDC, and also according to radiologist opinion on shape, margin, and echogenicity characteristics of segmented lesions. It is noted that the segmentation model, namely ResNet50, provides better segmentation based on objective and subjective assessment. It may be used in the healthcare system to identify thyroid nodules accurately in real time. [ABSTRACT FROM AUTHOR]

The purpose of this study was to enhance the detection accuracy for pine-wilt-diseased trees (PWDT) using time series UAV imagery (TSUI) and deep learning semantic segmentation (DLSS) techniques. The detailed methods to accomplish the research objectives were as follows. Considering the atypical and highly varied ecological characteristics of PWDT, DLSS algorithms of U-Net, SegNet, and DeepLab V3+ (ResNet18 and 50) were adopted. A total of 2350 PWDT were vectorized at 9 sites, and 795 images of 2000 damaged trees were used as training data and 200 images where 350 PWDT were found, were used as the test dataset. The felled trees were tracked and the pest-controlled trees were used as to ground truth the TSUI of at least 2 years to ensure the reliability of the constructed learning data. The results demonstrated that among the evaluated algorithms, DeepLab V3+ (ResNet50) achieved the best f1-score (0.742) and also provided the best recall (0.727). SegNet did not detect any shaded PWDT, but DeepLabV3+ (ResNet50) found most of the PWDT, especially those with atypical shapes near the felled trees. All algorithms except DeepLabV3+ (ResNet50) generated false positives for browned broadleaf trees. For the trees, all algorithms did not detect PWDT that had been dead for a long time and had lost most of their leaves or had turned gray. Most of the older PWDT have been logged, but for the few that remain, the relative lack of training data may be contributing to their poor detection. For land cover, the false positives occurred mainly in bare ground, shaded areas, roads, and rooftops. This study thus verified the potential use of semantic segmentation in the detection of forest diseases such as PWD, while the detection accuracy is anticipated to increase with the acquisition of adequate quantities of learning data in future. [ABSTRACT FROM AUTHOR]

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Diabetes mellitus is a metabolic disorder that causes a variety of vascular issues in the body. When the condition coexists with other general problems (high blood pressure, obesity, excessive cholesterol), the risk of ocular complications increases. Diabetes can damage the small blood vessels in the retina. This is referred to as diabetic retinopathy (DR). Therefore, the segmentation of the vascular network may assist in the automatic and early recognition and screening of DR. Since, manual vessel extraction is time-consuming, automation of this procedure is critical. In this paper, we focus on the implementation of different deep learning networks for accurate retinal vasculature semantic segmentation. We used three distinct models: SegNet, U-Net, and the convolutional neural network (CNN). These approaches were evaluated on three publicly available annotated databases: DRIVE, HRF, and CHASE-DB1. To compare the three proposed models with the previous methodologies, various metrics are calculated. The produced results were good and encouraging in terms of the quality of the visual diagnosis. [ABSTRACT FROM AUTHOR]

Lung cancer is seen as one of the most common lung diseases. For the patients having symptoms, the presence of lung nodules is checked by using various imaging techniques. Pulmonary nodules are detected in most of the cases having symptoms. But identifying the type of the nodule and the categorization still remains as a challenge. After confirming the presence of a nodule (benign or malignant) it takes several other steps to identify its characteristics. Improved imaging methods produce results within a short span of time. Research works are being conducted to increase the overall efficiency of the system. The proposed system considers authentic data sources for the study. The benign and malignant samples are considered for the generation of realistic large image sets. The generation of a large data set with the help of a generative adversarial network (GAN) is the first part of the work. The generated images using GAN cannot be differentiated from the original images even by a trained radiologist. This proves the importance of images generated using GAN. A GAN is able to generate 1024 × 1024 resolutions for natural images. Real data images are used to finetune the SegNet output. Through transfer learning, these weights are transferred to the system for segmentation of the images. The training process use real and generated images, which improve theefficiency of the network. The original data from LUNA 16 was used to further generate benign and malignant samples using GAN. A total of 440 images and their augmented images were used for training the GAN, and it generated 1,001,000 images. Hence the overall efficiency of the system was improved. To verify the results, the same various combinations and methods were considered and tabulated with various parameters. Methods with SegNet, GAN, and other combinations were evaluated to verify the efficiency of the system. Receiver operating characteristics were also plotted and compared with the area under the curve for verification of the results. [ABSTRACT FROM AUTHOR]

In the human body, white blood cells (WBCs) are crucial immune cells that help in the early detection of a variety of illnesses. Determination of the number of WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, as well as AIDS and leukemia. However, the conventional method of classifying and counting WBCs is time-consuming, laborious, and potentially erroneous. Therefore, this paper presents a computer-assisted automated method for recognizing and detecting WBC categories from blood images. Initially, the blood cell image is preprocessed and then segmented using an effective deep learning architecture called SegNet. Then, the important features are devised and extracted using the EfficientNet architecture. Finally, the WBCs are categorized into four different types using the XGBoost classifier: neutrophils, eosinophils, monocytes, and lymphocytes. The advantages of SegNet, EfficientNet, and XGBoost make the proposed model more robust and achieve a more efficient classification of the WBCs. The BCCD dataset is used to evaluate the performance of the proposed methodology, and the findings are compared to existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, and F1-score. Evaluation results show that the proposed approach has a higher rank-1 accuracy of 99.02% and outperformed other existing techniques. [ABSTRACT FROM AUTHOR]