A Novel Hybrid Framework for Enhanced Early Detection and Classification of Ocular Diseases: Integrating Deep Learning with Traditional Machine Learning Approaches

Ocular diseases pose a significant global health challenge, with early detection and accurate classification being crucial for effective treatment and prevention of vision loss. This study introduces an innovative hybrid framework that synergistically combines state-of-the-art deep learning architectures with traditional machine learning algorithms to advance the field of ocular disease detection and classification. The proposed approach achieves remarkable performance with an average accuracy of 96.8% across eight common ocular diseases, demonstrating a sensitivity of 95.6% and a specificity of 96.7%. These results highlight the potential of this hybrid model to improve significantly the diagnostic capabilities in ophthalmology. This research study offers a comprehensive and insightful narrative that integrates the key findings from the data analysis, making significant contributions to the field of ophthalmology and providing valuable guidance for future research endeavors.