Hybrid Framework for Advanced Ocular Disease Diagnosis

Abstract

Ocular diseases, a leading cause of vision impairment globally, necessitate early, accurate diagnosis for timely intervention. Conventional methods often face limitations in sensitivity and specificity.

To address this challenge, we propose a novel hybrid framework that synergistically integrates deep learning and traditional machine learning techniques. Our approach leverages the power of deep neural networks to extract intricate features from ocular images, while incorporating the robustness of traditional algorithms for enhanced classification.

The proposed framework achieved exceptional performance, surpassing existing methods with an average accuracy of 95.7% across eight common ocular diseases. This significant improvement demonstrates the potential of our approach to revolutionize ophthalmic diagnostics. Our findings offer valuable insights for future research and clinical practice, paving the way for more accurate and efficient detection and management of ocular diseases.