Eye Disease Classification Using VGG-19 Architecture

The deep learning methods, more especially the VGG-19 architecture, are used to categorize eye disorders. The objective is to create a model that is accurate and dependable for diagnosing a variety of eye conditions, including cataracts, diabetic retinopathy, and glaucoma. The VGG-19 network will be trained on a big dataset of eye fundus images, and its performance will be judged using a variety of metrics, such as accuracy, precision, recall, and F1-score. The efficiency of the VGG-19 architecture for categorizing eye diseases will be demonstrated by comparing the outcomes with those of other current deep learning models. The model established in this suggested system has the potential to help in the early diagnosis and management of eye illnesses, increasing patient outcomes and decreasing the load on healthcare systems.