Deep Learning-Based Detection of Diabetic Retinopathy Using Retina Images

Abstract

Early detection of retinal diseases, such as diabetic retinopathy, is crucial in preventing irreversible vision loss. This study presents an automated diagnostic system utilizing deep learning techniques, specifically Convolutional Neural Networks (CNN) and pre-trained models like Mobile Net and VGG16. These models analyze retinal fundus images to detect abnormalities, including microaneurysms and hemorrhages, which indicate the presence of retinal diseases. CNN facilitates efficient feature extraction, while MobileNet and VGG16 enhance disease classification accuracy. MobileNet, with its lightweight design, is optimized for real-time mobile applications, ensuring fast and efficient detection. In contrast, VGG16 offers higher precision but demands greater computational resources.The proposed system is trained and tested on publicly available datasets to ensure robustness across diverse retinal images. A comparative analysis of MobileNet and VGG16 is conducted, focusing on accuracy, sensitivity, and specificity in detecting retinal abnormalities. Experimental results highlight the system’s potential to assist healthcare professionals by automating the diagnostic process, enabling early detection and timely medical intervention. This approach reduces reliance on manual screening, offering a scalable and accessible solution for retinal disease diagnosis.