Cataract Detection using optimized VGG19 Model by Transfer Learning perspective and its Social Benefits

The common eye ailment known as a cataract causes the lens of the eye to become clouded, impairing vision. Normally transparent, the eye's lens has the function of focusing light onto the retina at the rear of the eye. Medical Surgery is frequently used to cure cataracts, which entails removing the clouded lens and substituting an artificial lens. The majority of patients who are vulnerable see a considerable improvement in their eyesight after cataract surgery, which is often safe and successful. As a result, lifestyle variables can be utilised to forecast the likelihood of cataracts. The goal of this study is to develop an image classification model based on exchange learning that can identify between a normal eye and one with cataracts. Proposed VGG19 model's outstanding classification capacity for cataract detection in the healthy human eye was proved by its accuracy rate of more than 90%.