Classifications of CNV, DME, Drusen and Normal in Retinal Optical Coherence using CNN

Abstract

In this research, the importance of Optical Coherence Tomography (OCT) in diagnosing and monitoring various retinal disorders, including Drusen, Diabetic Macular Edema (DME), and Choroidal Neovascularization (CNV), is highlighted. These conditions can have a significant impact on retinal health and vision. The research presents a technique that utilizes batch normalization for preprocessing OCT images. For classification of retinal disorders, the research employs the Inception v3 architecture, which is known for its effectiveness in image classification tasks. The performance of the proposed technique is evaluated using performance metrics such as sensitivity, specificity, accuracy, and precision. In this work, a total of 3,133 images were obtained from Kaggle.com. Among these, 710 images were classified as CNV, 895 as DME, 725 as drusen, and 804 as normal retinal images. Python was used for both designing and Google colab was used for executing the algorithm.