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This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]

Detection of small objects is one of the main challenges to be improved in deep learning, mainly due to the small number of pixels and scene's context, leading to a loss in performance. In this paper, we present an optimized approach based on deep object detection models that allow the detection of a higher number of elements and improve the score obtained for their class inference. The main advantage of the presented methodology is that it is not necessary to modify the internal structure of the selected convolutional neural network model or re-training for a specific scene. Our proposal is based on detecting initial regions to generate several sub-images using super-resolution (SR) techniques, increasing the number of pixels of the elements, and re-infer over these areas using the same pre-trained model. A reduced set of windows is calculated in the super-resolved image by analyzing a computed graph that describes the distances among the preliminary object detections. This analysis is done by finding maximal cliques on it. This way, the number of windows to be examined is diminished, significantly speeding up the detection process. This framework has been successfully tested on real traffic sequences obtained from the U.S. Department of Transportation. An increase of up to 44.6% is achieved, going from an average detection rate for the EfficientDet D4 model of 14.5% compared to 59.1% using the methodology presented for the first sequence. Qualitative experiments have also been performed over the Cityscapes and VisDrone datasets. [ABSTRACT FROM AUTHOR]

Face anti-spoofing has been attracting attention because of its prominent role in the security of face recognition systems. A face spoofing attack is launched on the authentication system by the attacker using the picture or video of the genuine user to get access to the services or information. This paper proposes local binary pattern (LBP) and convolutional neural network (CNN) based feature fusion model for classifying spoof face images. Initially, the images are pre-processed to remove the effect of the background in the feature extraction. The Viola-Jones algorithm is used for detecting and cropping the faces from the images. The color features of the pre-processed RGB images and LBP features are provided to the CNN. This feature combination reduces the requirement of a CNN-based deep learning architecture for providing a good classification performance. To validate the proposed model, it is trained and tested on NUAA and MSU-MFSD datasets. The experiment recorded the ACER of 0.21 and 0.20 on the MSU-MFSD and NUAA datasets, respectively. [ABSTRACT FROM AUTHOR]

Traditional navigation systems rely mainly on satellite navigation, but internal positioning and navigation cannot be achieved. This is mainly due to the inability to penetrate the wall due to the complex internal environment and the signal reaching the ground. Therefore, this paper proposes the research of indoor positioning algorithm based on improved convolutional neural network. The main method of this paper is based on the monocular vision indoor positioning based on the improved convolutional neural network. This method is an intelligent solution to the problems of traditional methods such as high cost, poor anti-interference ability, weak robustness, and poor compatibility. Positioning aids. The traditional computer vision camera pose estimation method is affected by the complex background in the image. When extracting feature corners, it is very affected by non-interest corners, so this paper adds an improved convolutional neural network algorithm to add in complex indoor scenes. With the region limitation, camera pose estimation in the region of interest is better to achieve low-cost, high accuracy and more stability. The improved model in this paper was tested on a test set of indoor datasets with markers, and the recognition accuracy reached 98.1%. In addition, this paper improves the PnP solution method, compares and analyzes the traditional RPnP, EPnP, and CEPPnP algorithms, and seeks the optimal camera pose estimation algorithm to locate the world coordinates of the current shooting position. The experimental results show that this paper proposes the positioning algorithm has high stability. [ABSTRACT FROM AUTHOR]

Deep learning has become the main solution for face recognition applications due to its high accuracy and robustness. In recent years, a batch of research on lightweight convolutional neural networks (CNNs) has emerged, bringing new ideas to the economic application of face recognition systems. In this paper, a lightweight face recognition algorithm is proposed to reduce the number of parameters and calculations of the face feature extraction network. The most important part of our approach lies in designing a novel inverted residual shuffle unit (IR-Shuffle). After being trained by ArcFace loss on a GPU workstation, our model built on improved IR-Shuffle blocks of size 1.45 MB achieves an accuracy of 98.65%. In terms of running time, our model is 5 ms faster than the current fastest MobileFaceNet, with only about 0.5% drop in accuracy. The proposed algorithm is implemented and optimized on the Jetson Nano embedded platform, and accurate and real-time deployment of the face recognition system is realized. The system takes 37 ms to perform the complete face detection and recognition and is robust to complex backgrounds and ambient light changes. Experimental results show that our system is of practical application value. [ABSTRACT FROM AUTHOR]

The goal of steganalysis is to unearth a concealed message hidden inside an innocent carrier by steganography. Steganography in the hands of unlawful people can pose a great threat to society. Secret message is embedded as a noise (residue) which silently disrupts the statistical nature of the carrier without visual transformation. For the purpose of unearthing covert communication, modeling this noise (residue) is a crucial factor. In this paper, a hybrid deep learning framework is proposed with convolutional neural network (CNN) to emphasize the need for residual based investigation for digital image steganalysis. In order to model the noise residuals, five types of handcrafted features are extracted, in which the first four types rely on acquiring noise residuals based on filtering by linear and nonlinear filters, and the fifth type relies on residual acquisition method based on Empirical Mode Decomposition (EMD). The extracted features are categorized using a robust CNN with a new parallel architecture that is capable of learning intricate details from input features to classify it as cover or stego. Mostly CNNs are trained with raw images, but in the proposed method, the 1-dimensional residual features are stacked into 2-dimensional grid and are then used to train the CNN. Three spatial content-adaptive and four spatial non-content-adaptive algorithms are used to evaluate the performance of the proposed architecture. The experimental results show the versatility and robustness of the proposed hybrid architecture towards these algorithms when compared to existing state-of-the-art steganalytic methods. A practical issue prevalent in deploying steganalysis in real-world is 'mismatch scenario,' and based on the experimentation done in this paper, the proposed architecture performs well under stego algorithm mismatch and payload mismatch scenarios. [ABSTRACT FROM AUTHOR]

With the leaps and bounds of computer performance and the advent of the era of big data, deep learning has drawn more and more attention from all walks of life. It can combine low-level features to form more abstract high-level features and describe the data more essentially. Therefore, it is widely used in various fields such as computer vision. Image segmentation is one of the most basic research topics in the field of computer vision. The main purpose is to extract regions of interest from images for later image processing research. In 3D reconstruction based on sequence images, the segmentation accuracy and speed of sequence images determine the quality and efficiency of target reconstruction. Therefore, when facing large-scale sequence images, the biggest problem is how to improve the segmentation speed while ensuring accuracy. Based on the above background, the research content of this article is an indoor scene segmentation algorithm based on full convolutional neural network. According to the characteristics of indoor application scenes, this paper proposes a fast convolutional neural network image segmentation method to segment the indoor scene image and construct the fast fully convolutional networks (FFCN) for indoor scene image segmentation uses inter-layer fusion to reduce the amount of network calculation parameters and avoid the loss of picture feature information by continuous convolution. In order to verify the effectiveness of the network, in this paper, a basic living object data set (XAUT data set) in an indoor environment is created. The XAUT data set is used to train the FFCN network under the Caffe framework to obtain an indoor scene segmentation model. In order to compare the effectiveness of the model, the structure of the worn FCN8s, FCN16s, and FCN32s models was fine-tuned, and the corresponding algorithm model for indoor scene segmentation was obtained by training with the XAUT data set. The experimental results show that the pixel recognition accuracy of all types of networks has reached 86%, and the mean IU ratio has reached more than 63%. The mean IU of the FCN8s network is the highest at 70.38%, but its segmentation speed is only 1/5 of FFCN. On the premise that other types of indicators are not much different, the average segmentation speed on FFCN fast segmentation convolutional neural network reaches 40 fps. It can be seen that the scale fusion technology can well avoid the loss of image feature information in the network convolution and reddening process. Compared with other FCN networks, it has a faster speed and is conducive to real-time image preprocessing. [ABSTRACT FROM AUTHOR]

The performance of target detection algorithms for manual features is gradually saturated, and the development of target detection has stagnated. Computer vision is a discipline that studies how to use computers to replace the human eye and use visual information and visual algorithms to automatically detect, recognize, and track target objects. Target detection has also been fully developed as a basis for solving advanced vision tasks such as target tracking, instance segmentation, image understanding, and behavior recognition. The theories of artificial intelligence and deep learning has gradually developed, making convolutional neural networks a hot spot of attention in the field of computer vision and image processing. Based on the work in the field of target detection, this paper proposes a target detection algorithm based on the prior knowledge of the impact area of the tennis ball. Our experiments and evaluations show that the algorithm can achieve higher detection accuracy and faster detection speed. Moreover, the proposed algorithm accurately tracks and judge the impact area of tennis sports, and improve the accuracy and reliability of the tennis impact. [ABSTRACT FROM AUTHOR]

Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts. [ABSTRACT FROM AUTHOR]

Macular edema (ME) is an essential sort of macular issue caused due to the storing of fluid underneath the macula. Age-related Macular Degeneration (AMD) and diabetic macular edema (DME) are the two customary visual contaminations that can lead to fragmentary or complete vision loss. This paper proposes a deep learning-based predictive algorithm that can be used to detect the presence of a Subretinal hemorrhage. Region Convolutional Neural Network (R-CNN) and faster R-CNN are used to develop the predictive algorithm that can improve the classification accuracy. This method initially detects the presence of Subretinal hemorrhage, and it then segments the Region of Interest (ROI) by a semantic segmentation process. The segmented ROI is applied to a predictive algorithm which is derived from the Fast Region Convolutional Neural Network algorithm, that can categorize the Subretinal hemorrhage as responsive or non-responsive. The dataset, provided by a medical institution, comprised of optical coherence tomography (OCT) images of both pre- and post-treatment images, was used for training the proposed Faster Region Convolutional Neural Network (Faster R-CNN). We also used the Kaggle dataset for performance comparison with the traditional methods that are derived from the convolutional neural network (CNN) algorithm. The evaluation results using the Kaggle dataset and the hospital images provide an average sensitivity, selectivity, and accuracy of 85.3%, 89.64%, and 93.48% respectively. Further, the proposed method provides a time complexity in testing as 2.64s, which is less than the traditional schemes like CNN, R-CNN, and Fast R-CNN. [ABSTRACT FROM AUTHOR]

Cloud computing helps users to store and retrieve their data in the cloud online on an as-per-pay basis anytime, anywhere in the world. As a consequence, the security of data stored in the cloud serves as a key concern for cloud consumers due to ongoing hacking incidents in the cloud. Both cloud service providers (CSPs) and consumers face various attacks that pose a serious threat to both the organization and the user. Therefore, a robust intrusion detection system (IDS) needs to be configured to identify and avoid potential attacks in the cloud at an earlier stage. The state-of-the-art IDS system which used shallow machine learning techniques suffered from poor accuracy with a higher false alarm rate and higher response time. To overcome this problem, a hybrid semantic deep learning (HSDL) architecture is developed in this paper by integrating the long short-term memory (LSTM), convolutional neural network (CNN), and support vector machine (SVM) architectures. The semantic information present in the network traffic is identified using a semantic layer known as the Word2Vec embedding layer. The HSDL model classifies the intrusion present in the text along with its corresponding attack class. The normal text without any trace of intrusion is encrypted using an advanced encryption standard (AES) algorithm to enhance cloud storage security, and the optimal key with the largest key breaking time for the AES algorithm is selected using the crossover-based mine blast optimization algorithm (CMBA). The proposed HSDL scheme is evaluated and tested using two real-time intrusion detection benchmark datasets, namely NSL-KDD and UNSW-NB15. The proposed model achieves an accuracy of 99.98% for the NSL-KDD and 98.47% for the UNSW-NB15 dataset, respectively. The experimental and security analysis conducted proves the efficiency and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Person re-identification (Re-ID) across multi-camera views remains a very challenging task. In fact, the use of global convolutional neural network (CNN) features is not optimal since the appearance similarity of identities is overlooked. Hence, persons with similar global appearance (wearing in very similar clothes) may be confused and hard to distinguish. To address these shared challenges, some attribute labels (such as hat or bag) can improve the discriminative ability of Re-ID model by depicting person images with more detailed descriptions. Hence, we propose in this paper a new attributes recognition model, called ARN, based on a deep CNN model. Then, we propose a new approach based on attributes similarity and k-reciprocal re-ranking algorithm in order to enhance person Re-ID baseline performances. The proposed approach, called K-RNNA, is motivated first of all, by the potential of re-ranking methods to resort the 'baseline' re-identification results from the initial retrieval. Second, attributes, as a mid-level semantic representation of the person's local appearance, could also potentially resolve these challenges. In fact, learned semantic attributes such as gender, hair, accessories (hat, bags, backpack, etc.) are used as a supplement during the re-ranking phase in order to discriminate between identities with similar global appearance. The experimental study conducted on Market-1501, Duke-MTMC-reID and CUHK03 datasets confirms that when attributes are used as supplement in the verification phase, we improve considerably the person Re-ID performances and we reach competitive results in terms of mean average precision (mAP) and Rank-1. [ABSTRACT FROM AUTHOR]

Agricultural machines (AMs) refer to equipment usually used in agriculture such as tractors, hand tools, and power tools. It reduces the labor work, increases farms produce, enhances goods quality, and reduces farming time and cost-saving. However, the faults in the fuel system, blades, engine of the AM will often result in degraded vehicle performance, compromising the vehicle's efficiency and strength. To overcome these problems, fault detection algorithms are developed to identify the faults even before they occur with high classification accuracy. The deep convolutional neural network (DCNN) is a popular deep learning model that offers a high classification recognition rate, and it is widely adopted in similar fields for monitoring the health status of machines. Very few state-of-the-art works are available to identify the health state of agricultural machines using deep learning techniques and extracting the acoustic features from an audio recording. The acoustic signal-based agricultural machine health monitoring and fault prediction model using smartphones is a cost-effective option that is deployed in this proposed work. To optimize the network structure of the DCNN, this paper proposes a Levy flight optimization algorithm (LFOA). The DCNN-LFOA model is implemented on the smartphone's on-board device (OBD) along with the health monitoring application. The LFOA algorithm minimizes the number of neurons in the DCNN hidden layer and the number of input features from the audio recordings and enhances the classification accuracy. The LFOA algorithm provides the optimal solution which is essential in developing a lightweight DCNN model to implement in the edge processor (smartphone). The experimental results prove that the proposed model gives improved accuracy for the six faults to be classified and serves as a new research model to identify the health condition of the vehicles. [ABSTRACT FROM AUTHOR]

Vehicle positioning and vehicle identification of natural scene images are an important part of intelligent transportation systems and unmanned driving research. In current situation, there are still some problems in vehicle intelligent wireless positioning. In order to improve the intelligent wireless positioning efficiency of vehicles, based on the convolutional neural network, this research combines the concept of deep learning to carry out algorithm innovation in the research. Moreover, this paper combines the actual vehicle positioning problem points to collect data, simulates the vehicle positioning situation in a variety of complex situations, and designs a controlled test to verify. The results show that the algorithm of this study has certain effects, which can provide reference for subsequent related research and has certain practical significance. [ABSTRACT FROM AUTHOR]

Migration deconvolution is an image domain approach to least-squares migration, which is considered the state-of-the-art algorithm for obtaining seismic reflectivity models of the earth from seismic acquisition results. Seismic imaging is an active research field with the development over the last few years of several techniques that have mitigated imaging issues. Ongoing research aims to improve image resolution and thus provide a more reliable seismic amplitude for the interpreter. Migration deconvolution can be framed as an inverse problem in the image domain to mitigate image resolution problems and reduce migration artifacts. This paper presents a migration deconvolution method via deep learning based on the Hessian filter least-squares migration (HF-LSM) algorithm. The idea is to use deep learning techniques to model the inverse operator instead of directly estimating the inverse Hessian matrix. A data set is generated from a given velocity model by applying Born modeling to the migrated image, followed by application of the reverse time migration algorithm. The resultant data set is then used to train several neural network models. The networks learn the blurring operator that describes the image degradation due to the effects of acquisition geometry. Three different network topologies were developed to handle this problem: a simple fully convolutional neural network, a U-Net and a generative adversarial network. Our results show that the proposed approach provides images of higher resolution and superior quality than the traditional HF-LSM workflow. [ABSTRACT FROM AUTHOR]

Delineation of bed boundaries based on resistivity logging curves is important prior information for the inversion and interpretation of resistivity logging data. Traditionally, the layering algorithm mainly use the derivatives of resistivity curves or other logging methods as reference. However, measurement error or resolution mismatch may lead to misjudgment of the boundary. In view of the shortcomings of traditional methods, this paper presents an automatic layering algorithm of array induction logging curves based on deep learning. In this algorithm, a locally connected convolution neural network is used, and the generalization ability of the network is improved by enlarging the training set, optimizing the window length and threshold, and strengthening the layering effect. Simulation and field data show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient's anxiety. Recently, Deep Learning Neuronal Network (DL) has been suggested in order to reconstruct MRI scans with conserving the structural details and improving parallel imaging-based fast MRI. In this paper, we propose Deep Convolutional Encoder-Decoder architecture for CS-MRI reconstruction. Such architecture bridges the gap between the non-learning techniques, using data from only one image, and approaches using large training data. The proposed approach is based on autoencoder architecture divided into two parts: an encoder and a decoder. The encoder as well as the decoder has essentially three convolutional blocks. The proposed architecture has been evaluated through two databases: Hammersmith dataset (for the normal scans) and MICCAI 2018 (for pathological MRI). Moreover, we extend our model to cope with noisy pathological MRI scans. The normalized mean square error (NMSE), the peak-to-noise ratio (PSNR), and the structural similarity index (SSIM) have been adopted as evaluation metrics in order to evaluate the proposed architecture performance and to make a comparative study with the state-of-the-art reconstruction algorithms. The higher PSNR and SSIM values as well as the lowest NMSE values could attest that the proposed architecture offers better reconstruction and preserves textural image details. Furthermore, the running time is about 0.8 s, which is suitable for real-time processing. Such results could encourage the neurologist to adopt it in their clinical routines. Graphical abstract. [ABSTRACT FROM AUTHOR]

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data. Graphical abstract. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive suite of techniques for optimized memory management in multi-GPU systems to accelerate deep learning application execution. We employ a hybrid utilization of GPU and CPU memories in a multi-GPU environment by effectively addressing contention issues in the shared interconnect (e.g., PCIe, NVLink). In addition, we designed and implemented an intelligent prefetching algorithm (from CPU memory to GPU) that achieves the highest processing throughput while sustaining a large mini-batch size. We successfully implemented our optimization techniques on TensorFlow, and performed extensive experiments in various multi-GPU environments including traditional PCIe and the latest high-bandwidth interconnect, NVLink. Evaluation results show that our proposed scheme actually improves computing performance by decreasing the I/O bottleneck, and effectively increasing the mini-batch size without sacrificing overall training throughput. [ABSTRACT FROM AUTHOR]

Machine learning era began to rule almost all the technologies, which influences the improvement of the performance due to its intelligent computing methodologies. Especially the Deep Learning algorithm plays a vital role in computing a human-like decision, which is considered to be the superior breakthrough technology of the century. Deep Learning algorithms generate a massive sum of features which is stacked and learned by many other neurons of the network in the term of links. Links initiated from the input and ends in the output of the network connecting many neurons on its path. The significant limitation of this network is its thirst towards the high computation power. This paper represents a methodology to make the system consume less computation requirement during its training or testing phase. In this process, an effective clustering algorithm (fraternal K-median clustering) is used as the preprocessing strategies, and as the second phase the dropout regularization procedure is implemented (in the Convolutional Neural Network, a type of Deep Learning algorithm) to eliminate most of the insignificant data. The dropout strategies used in the process helps in the improvement of accuracy by making the network overfit the decision of CNN, obtaining state-of-the-art results. [ABSTRACT FROM AUTHOR]

A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers. Graphical abstract A/N Deep Learning Classifier. [ABSTRACT FROM AUTHOR]

With the advent of biomedical imaging technology, the number of captured and stored biomedical images is rapidly increasing day by day in hospitals, imaging laboratories and biomedical institutions. Therefore, more robust biomedical image analysis technology is needed to meet the requirement of the diagnosis and classification of various kinds of diseases using biomedical images. However, the current biomedical image classification methods and general non-biomedical image classifiers cannot extract more compact biomedical image features or capture the tiny differences between similar images with different types of diseases from the same category. In this paper, we propose a novel fused convolutional neural network to develop a more accurate and highly efficient classifier for biomedical images, which combines shallow layer features and deep layer features from the proposed deep neural network architecture. In the analysis, it was observed that the shallow layers provided more detailed local features, which could distinguish different diseases in the same category, while the deep layers could convey more high-level semantic information used to classify the diseases among the various categories. A detailed comparison of our approach with traditional classification algorithms and popular deep classifiers across several public biomedical image datasets showed the superior performance of our proposed method for biomedical image classification. In addition, we also evaluated the performance of our method in modality classification of medical images using the ImageCLEFmed dataset. Graphical abstract The graphical abstract shows the fused, deep convolutional neural network architecture proposed for biomedical image classification. In the architecture, we can clearly see the feature-fusing process going from shallow layers and the deep layers. [ABSTRACT FROM AUTHOR]

Cerebrovascular accident due to carotid artery disease is the most common cause of death in developed countries following heart disease and cancer. For a reliable early detection of atherosclerosis, Intima Media Thickness (IMT) measurement and classification are important. A new method for decision support purpose for the classification of IMT was proposed in this study. Ultrasound images are used for IMT measurements. Images are classified and evaluated by experts. This is a manual procedure, so it causes subjectivity and variability in the IMT classification. Instead, this article proposes a methodology based on artificial intelligence methods for IMT classification. For this purpose, a deep learning strategy with multiple hidden layers has been developed. In order to create the proposed model, convolutional neural network algorithm, which is frequently used in image classification problems, is used. 501 ultrasound images from 153 patients were used to test the model. The images are classified by two specialists, then the model is trained and tested on the images, and the results are explained. The deep learning model in the study achieved an accuracy of 89.1% in the IMT classification with 89% sensitivity and 88% specificity. Thus, the assessments in this paper have shown that this methodology performs reasonable results for IMT classification. [ABSTRACT FROM AUTHOR]