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

Abstract

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 are caused by pathological alterations in the retina. The importance of early diagnosis in the management of these illnesses cannot be overstated. In this article, an approach based on convolutional neural networks (CNN), a deep learning method, is presented for the detection of retinal disorders from OCT images. A new CNN architecture has been developed for disease diagnosis and classification. The proposed method has been found to have an accuracy rate of 94% in the detection of retinal disorders. The results are obtained by comparing the proposed CNN network model in a deep learning application used in classification with the MobileNet50 network model in the literature. The evaluation parameter values for models trained using the 5-fold cross validation approach for each type of disease in the retinal OCT image dataset are also submitted. 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.