A Comprehensive Survey on Detection of Ocular and Non-Ocular Diseases Using Color Fundus Images

Abstract

This study examines the application of color fundus imaging for detecting both ocular and non-ocular diseases using recent advancements in machine learning techniques. Fundus images provide essential diagnostic information for a range of retinal and systemic conditions. The review systematically explores various methods for diagnosing major eye diseases like diabetic retinopathy (DR), age related macular degeneration (AMD), glaucoma and retinal vein occlusion (RVO) as well as non-ocular disorders like cardiovascular disease (CVD) using fundus images. It includes a thorough comparison of machine learning and deep learning models, assessing their performance through metrics such as accuracy, sensitivity, specificity, precision, recall, and Area Under the Curve (AUC). The study also incorporates a dataset comparison table, evaluating the attributes and suitability of different datasets for specific diagnostic tasks. EfficientNetB0 is highlighted as a top performer for diabetic retinopathy detection, while methods like CNN-LSTM combinations, Grad-CAM, and Swin Transformers show promise in detecting AMD, glaucoma, and RVO, respectively. Additionally, models such as CNNs, DNNs, and DenseNet-169 have been effective for CVD detection through analysis of retinal images. The review examines strategies to advance detection techniques for each disease, with a particular emphasis on future research directions aimed at improving early disease detection.