Early Detection of EYE Cancer from Retinal Images

Globally, ocular fundus diseases—such as glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR)—are common causes of vision impairment. Retinal vein occlusion (RVO), retinal artery occlusion (RAO), retinal detachment (RD), and fundus tumours are uncommon but serious ailments that provide substantial obstacles, particularly in rural and disadvantaged locations. Early and accurate detection of such diseases is crucial for effective treatment and prevention of severe vision loss. This study aims to develop a deep learning model for the automated detection of eye cancer from retinal images, leveraging convolutional neural networks (CNNs) to enhance diagnostic accuracy and accessibility. This research highlights the potential of deep learning in early eye cancer detection, offering a step towards more accessible and reliable diagnostic tools in ophthalmology. The integration of such technologies can significantly improve patient outcomes, particularly in regions with limited access to specialized healthcare services.