Automated Identification of Retinal Disorders by Deep Learning with Optical Coherence Tomography Pictures

Abstract

A crucial problem in computer-aided diagnosis (CAD) for medical applications is the classification of retinal disorders. This paper addresses the 4-class classification problem in optical coherence tomography image analysis (OCT) to automatically identify choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL. Retinal OCT pictures were identified by the proposed classification approach using a set of four classification model instances, each based on an upgraded residual neural network (ResNet50).At the patient level, the experiment employs a 10-fold cross-validation process that is founded on the growth of the OCT retinal picture collection. According to the study's findings, multiple ResNet50 concatenation is a helpful method when the availability of pictures used in medicine is scarce. In order to better understand the decision-making process, we also ran a bottleneck test and a qualitative assessment of the model predictions.