International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Diabetic Retinopathy (DR) is a severe microvascular complication of diabetes and one of the leading causes of preventable blindness worldwide. Early diagnosis and accurate classification of DR stages are essential for timely medical intervention and effective treatment. However, traditional manual screening of retinal fundus images by ophthalmologists is time-consuming, subjective, and prone to inter-observer variability. This paper presents an automated system for the Analysis And Classification Of Diabetic Retinopathy using Deep Learning techniques. The proposed approach utilizes retinal fundus images collected from standard public datasets such as Kaggle and clinically sourced datasets. Image preprocessing and enhancement techniques are applied to improve retinal feature visibility, followed by deep feature extraction using a Convolutional Neural Network (CNN) based on the ResNet50 architecture. The trained model classifies retinal images into distinct DR severity stages, including No Diabetic Retinopathy, Mild Diabetic Retinopathy, and Severe Diabetic Retinopathy. Experimental evaluation demonstrates that the proposed system achieves high classification accuracy, sensitivity, and specificity, making it suitable for real-world screening applications. A user-friendly graphical interface is also developed to assist clinicians by providing rapid and reliable DR predictions. The proposed system serves as an effective computer-aided diagnosis tool, reducing screening workload and improving early detection of diabetic eye diseases.

Keywords: Diabetic Retinopathy, Deep Learning, Convolutional Neural Networks, ResNet50, Medical Image Analysis, Retinal Fundus Images, Computer-Aided Diagnosis

Downloads: | DOI: 10.17148/IJARCCE.2025.141296