Deep Model Exposure and Classification of Diabetic Retinopathy Using Fundus Imaging

Diabetic retinopathy is the most common complication of diabetes mellitus and a major cause of preventable blindness worldwide. Despite significant advances, effective treatment options remain limited, especially for proliferative diabetic retinopathy and diabetic macular edema. This paper presents a new Prognosis of Microaneurysm and Non-Proliferative Diabetic Retinopathy (PMNPDR) detection framework based on deep convolutional neural networks (CNNs) for semantic segmentation of retinal fundus images. Using integration of sophisticated technologies such as convolutional 3D layers, pooling, and activation with ReLU gives the potential feature extraction ability which reduces overfitting. By using PMNPDR architecture the accuracy comes around 99.6 % followed by 98.11 % and 99.32 % with sensitivity and specificity respectively, indicating a drastic improved performance against most of the commonly used architectures for example, AlexNet, VGGNet and GoogleNet, etc. Accurate classification of DR stages by the system has proved that deep learning models can transform early diagnosis and thus lead to timely interventions with better patient outcomes. This study underlines the importance of harnessing AI-driven solutions for superior diagnostic accuracy and efficiency in combating DR.