Retinal Disease Classification Using Custom CNN Model From OCT Images

Abstract

Retinal diseases affect the eye’s retina and are the leading cause of vision loss. Timely detection and diagnosis can provide more treatment options and preserve vision. Retinal imaging, such as Optical Coherence Tomography (OCT), is used for screening patients and enables clinicians to visualize the retinal layers in detail, helping in early diagnosis and tailoring appropriate treatment plans. This study uses OCT images to detect diseases related to macular disorders common in old age. A custom convolution Neural Network (CNN) model is proposed in this study to classify OCT images of normal, choroidal neovascularization (CNV), diabetic macular edema (DME), and Drusen from a public dataset. Experiments were conducted using conventional machine learning (ML) and transfer learning-based models other than the proposed one. The proposed model performed favorably, attaining the training and validation accuracy of 97% and 93%, respectively. The proposed model achieves a testing accuracy of 98% with a loss of 0.051 and outperforms the existing models compared to this study.