Advanced Deep Learning Techniques for Evaluating OCT Image Quality and Detecting Retinal Pathologies

Abstract

Diabetic macular edema (DME) and age-related macular degeneration (AMD) are major causes of vision impairment and blindness. While many classification applications for these diseases achieve high performance, they often overlook the crucial aspect of dataset and image quality, leading to potential erroneous predictions. This study emphasizes the importance of data quality in medical image classification, specifically for retinal imaging. We propose an Optical Coherence Tomography (OCT) image quality evaluation model using the pre-trained ARNIQA (leArning distoRtion maNifold for Image Quality Assessment) model to accurately identify retinal diseases autonomously. Our methodology includes a three-class classification system utilizing two Convolutional Neural Network (CNN) models, ResNet50 and Xception, applied to three datasets: the original dataset, a subset of high-quality images, and a subset of low-quality images. Using a Tunisian OCT dataset of 2887 images, we demonstrate the efficacy of our approach, achieving $100 \%$ accuracy with highquality images.