THE USE OF NEURAL NETWORKS IN THE DIAGNOSIS OF DIABETIC RETINOPATHY - E.R. Dobrov (Модели, системы, сети в экономике, технике, природе и обществе, 2022)
Background. One of the serious consequences of diabetes is the negative impact on the visual system. The aim of the work is to review the sources devoted to the task of diagnosing diabetic retinopathy from eye images using neural networks. Materials and methods. The use of modern methods, approaches and algorithms at such stages as data collection and preparation, data preprocessing, image recognition task, transfer training, comparison of methods, ensembles of models, system development is considered. Possible promising further steps in the future research are outlined. Results. In the process of analyzing publications on methods of diagnosing diabetic retinopathy from eye images based on neural networks, the following areas were identified to improve the existing results: increasing image data sets, image preprocessing methods, interpretation of the neural network model, computational power of algorithms for implementation on mobile devices, classification and segmentation tasks of eye lesions, false-negative and false-positive diagnoses, ensembles of models, the use of recurrent and capsule neural networks. Conclusions. Based on the results of the study, the directions for improving the achievements in the task of eye image recognition for the diagnosis of diabetic retinopathy were identified.