Published Paper Details:

The early identification of ocular diseases--namely cataract, diabetic retinopathy (DR), and glaucoma--is vital for preventing permanent vision loss, especially among elderly individuals and patients with diabetes. With the rising global prevalence of these conditions, there is an urgent need for scalable and accurate screening solutions. Over the past few years, deep learning has become a reliable approach for recognizing diseases by processing and interpreting medical images automatically. This report investigates the role of deep learning in the early diagnosis of cataract, DR, and glaucoma, focusing on critical aspects such as image acquisition, data preprocessing, model architecture, and clinical applicability. Modern AI architectures, like convolutional neural networks and vision transformers, have proven highly effective in examining intricate visual data from retinal and ocular scans. Moreover, the report discusses the challenges related to dataset variability, imbalance, and annotation, as well as the importance of explainability and validation in clinical environments. As the field progresses, the integration of deep learning-based tools into routine ophthalmic care holds the potential to enhance diagnostic accuracy, reduce workload for healthcare professionals, and improve outcomes for patients worldwide.