Machine Learning-Based Detection and Classification of Eye Diseases: A Comprehensive Review and Novel Algorithm - Raut, Prashant; Babar, Sachin; Patil , Shivprasad; Mahalle, Parikshit (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Machine learning has transformed the landscape of ophthalmology, offering a powerful approach for automating and improving the detection and classification of eye diseases. This comprehensive review delves into the current state of the field, emphasizing the potential and challenges. Central to this review is the concept of the "Vision Transfer Method," a novel approach that leverages the transfer of learned visual knowledge to enhance disease detection and classification. We explore the utilization of the Vision Transfer Method in the analysis of diverse ophthalmic imaging data, encompassing retinal images, optical coherence tomography (OCT) scans, and fundus photographs. Our analysis underscores the critical need for extensive and diverse datasets and the interpretability of machine learning models in clinical practice. Ethical considerations and regulatory compliance are discussed, ensuring responsible implementation of this transformative technology. Additionally, this paper introduces a novel diagnostic algorithm based on the Vision Transfer Method, poised to significantly enhance diagnostic accuracy and early disease identification, ultimately contributing to improved patient outcomes in the domain of ophthalmology.