A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images

Abstract

The retina is a network layer that covers the back wall of the human eye and is made up of visual cells. The retina layer is the most complex and sensitive part of the eye, and disorders that affect it have a big impact on people's lives. The Optical Coherence Tomography (OCT) imaging technology can be used to diagnose diseases that arise because of pathological alterations in the retina. The importance of early diagnosis in the management of these illnesses cannot be overstated.
 In this article, a approach based on convolutional neural networks (CNN), a deep learning method, is presented for the detecting of retinal disorders from OCT images, based on the literature review. For disease detection and categorization, a CNN architecture is developed. The proposed method has been found to have a 94 percent accuracy rate in the identification of retinal disorders. In a deep learning application for classification, the proposed CNN network model is compared to the MobileNet50 network model available in the literature, and the results are provided. The evaluation parameter values for models trained using the 5-fold cross-validation approach for each type of disease in the retinal OCT images dataset are also included in the publication. The proposed method can clearly be utilized as a decision-making tool to assist clinicians in diagnosing retinal illnesses in a clinical context, based on its effectiveness thus far.