OcularInsight-VGG16 Powered Multilabel Disease Detection

The objective of the Eye Disease Detection System is to present an automated approach for the timely detection and diagnosis of eye ailments using deep learning techniques. Ailments such as cataracts play a significant role in the worldwide prevalence of vision impairment and blindness. Prompt identification and intervention are essential in halting further deterioration of vision and maintaining ocular health. Nonetheless, the manual scrutiny of fundus images by healthcare practitioners can be arduous and susceptible to biases. To address this challenge, our research employs transfer learning with the VGG16 convolutional neural network to scrutinize fundus images and anticipate the existence and nature of eye disorders. The system encompasses a user-friendly online platform that permits users to upload fundus images, examine prediction outcomes, and provide input to enhance the model's efficacy. The dataset employed for training and assessing the model consists of 5,000 labeled fundus images, encompassing a range of ocular ailments and normal states.