Detection and Classification of Non-Proliferation Diabetic Retinopathy using VGG-19 CNN Algorithm

Abstract

Microvascular leakage within the retina causes the illness known as diabetic retinopathy (DR) in the eye. For people with diabetes mellitus (DM), diabetic retinopathy is the main reason for vision loss. This Disease is a global health issue, as the condition can lead to long-term disability and decreased quality of life for affected individuals. As a result, It causes microvascular issues and irreversible vision loss due to increase in sugar levels. Unfortunately, the accuracy of existing approaches is limited because of issues such as inadequate contrast, imaging quality, and lesion unpredictability. We propose a VGG-19 convolutional neural network technique for the identification and classification of NPDR in this research. Overcoming these obstacles, our goal is to design a system that can detect and classify NPDR from retinal pictures. Our findings show that our proposed technique is effective in reaching high accuracy and might potentially contribute to the early identification and treatment of NPDR. We also created a user interface for classification and detection of the severity of the disease.