A Review on Multiple-Ocular Disease Detection Methodology using ML and DL Techniques

Abstract

The rapid advancement in machine learning (ML) and deep learning (DL) techniques has significantly impacted the detection and diagnosis of ocular diseases, which are critical for preserving vision and overall eye health. This review aims to explore the various ML and DL methodologies applied to the detection of multiple ocular diseases, highlighting their effectiveness, limitations, and areas for improvement. The motivation behind this review stems from the increasing prevalence of ocular diseases and the need for efficient, accurate diagnostic tools. Despite the promising results of existing techniques, limitations such as data variability, the need for extensive training data, and computational resource requirements persist. The objective is to synthesize current methodologies and propose enhancements, particularly through the integration of attention mechanisms in convolutional neural networks (CNNs). This review identifies gaps in current research and suggests directions for future work to enhance diagnostic accuracy and clinical applicability.