Wavelet scattering transform application in classification of retinal abnormalities using OCT images.

To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were [Formula: see text] and [Formula: see text], respectively. We achieved an accuracy of [Formula: see text] in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of [Formula: see text] and [Formula: see text], respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity.

An Emotion Recognition Embedded System using a Lightweight Deep Learning Model.

Background: Diagnosing emotional states would improve human-computer interaction (HCI) systems to be more effective in practice. Correlations between Electroencephalography (EEG) signals and emotions have been shown in various research; therefore, EEG signal-based methods are the most accurate and informative. Methods: In this study, three Convolutional Neural Network (CNN) models, EEGNet, ShallowConvNet and DeepConvNet, which are appropriate for processing EEG signals, are applied to diagnose emotions. We use baseline removal preprocessing to improve classification accuracy. Each network is assessed in two setting ways: subject-dependent and subject-independent. We improve the selected CNN model to be lightweight and implementable on a Raspberry Pi processor. The emotional states are recognized for every three-second epoch of received signals on the embedded system, which can be applied in real-time usage in practice. Results: Average classification accuracies of 99.10% in the valence and 99.20% in the arousal for subject-dependent and 90.76% in the valence and 90.94% in the arousal for subject independent were achieved on the well-known DEAP dataset. Conclusion: Comparison of the results with the related works shows that a highly accurate and implementable model has been achieved for practice.

Applications and Challenges of Telemedicine: Privacy-Preservation as a Case Study.

Today, technology has an important impact on the development of medical services, especially during the outbreak of COVID-19. Telemedicine, known by terms such as telehealth and digital health, refers to the utilization of technology to provide health care services at a distance that leads to improved monitoring, detecting and treatment of disease, and provision of individual care. It has been considered in various fields such as radiology, cardiology, pulmonology, psychiatry, emergency care and surgery. The most important advantages of using telemedicine are saving time for the doctor and the patient, reducing the cost of multiple visits to the doctor, reducing the spread of contagious diseases and caring for patients who cannot see a doctor, such as the elderly. In this paper, we review the research in the field of applying telemedicine, as well as its advantages and disadvantages. Next, we discuss the challenges in the field of using telemedicine which are privacy preserving, data security, cost of infrastructures, lack of physical examination and responsibility for patients' compensation. One of the most important challenges is privacy preserving of patients' information during transmission and process. We categorize and compare the various methods that have been proposed to protect peoples' privacy.

Detection of Retinal Abnormalities in OCT Images Using Wavelet Scattering Network.

Diagnosis retinal abnormalities in Optical Coherence Tomography (OCT) images assist ophthalmologist in the early detection and treatment of patients. To do this, different Computer Aided Diagnosis (CAD) methods based on machine learning and deep learning algorithms have been proposed. In this paper, wavelet scattering network is used to identify normal retina and four pathologies namely, Central Serous Retinopathy (CSR), Macular Hole (MH), Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR). Wavelet scattering network is a particular convolutional network which is formed from cascading wavelet transform with nonlinear modulus and averaging operators. This transform generates sparse, translation invariant and deformation stable representations of signals. Filters in the layers of this network are predefined wavelets and not need to be learned which causes decreasing the processing time and complexity. The extracted features are fed to a Principal Component Analysis (PCA) classifier. The results of this research show the accuracy of 97.4% and 100% in diagnosis abnormal retina and DR from normal ones, respectively. We also achieved the accuracy of 84.2% in classifying OCT images to five classes of normal, CSR, MH, AMD and DR which outperforms other state of the art methods with high computational complexity. Clinical Relevance- Clinically, the manually checking of each OCT B-scan by ophthalmologists is tedious and time consuming and may lead to an erroneous decision specially for multiclass problems. In this study, a low complexity CAD system for retinal OCT image classification based on wavelet scattering network is introduced which can be learned by a small number of data.