Retina Disorders Classification via OCT Scan: A Comparative Study between Self-Supervised Learning and Transfer Learning

Abstract

Retina disorders are among the common types of eye disease that occur due to several reasons such as aging, diabetes and premature born. Besides, Optical Coherence Tomography (OCT) is a medical imaging method that serves as a vehicle for capturing volumetric scans of the human eye retina for diagnoses purposes. This research compared two pretraining approaches including Self-Supervised Learning (SSL) and Transfer Learning (TL) to train ResNet34 neural architecture aiming at building computer aided diagnoses tool for retina disorders recognition. In addition, the research methodology employs convolutional auto-encoder model as a generative SSL pretraining method. The research efforts are implemented on a dataset that contains 109,309 retina OCT images with three medical conditions including Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), DRUSEN as well as NORMAL condition. The research outcomes showed better performance in terms of overall accuracy, sensitivity and specificity, namely, 95.2%, 95.2% and 98.4% respectively for SSL ResNet34 in comparison to scores of 90.7%, 90.7% and 96.9% respectively for TL ResNet34. In addition, SSL pretraining approach showed significant reduction in the number of epochs required for training in comparison to both TL pretraining as well as the previous research performed on the same dataset with comparable performance.