A Tutorial on Automated Classification of Eye Diseases Using Deep Learning

Sight is one of the five senses essential to human experience, and the eyes are vital organs that require careful protection. These organs are also susceptible to a variety of diseases, some of which may develop without obvious external symptoms, necessitating specialized imaging and diagnostic techniques. Conversely, other conditions present visible signs that can be observed directly. This paper presents a practical approach to the identification of thirteen well-known eye diseases-cataract, corneal neovascularization, corneal ulcer, dry eye, endophthalmitis, globe rupture, Graves ophthalmopathy, ptosis, scleritis, strabismus, stye, uveitis, and xanthelasma-based on visual symptoms. Using transfer learning with the ResNet152V2 deep learning model, we demonstrate an average validation accuracy of 98.8%. The methodology is presented in a reproducible, step-by-step format suitable for educational purposes, allowing opticians, general practitioners, and learners to explore automated eye disease diagnosis. All code, datasets, and procedures are documented to facilitate practical learning and replication.