DEEPDIABETIC :AN IDENTIFICATION SYSTEM OF DIABETIC EYE DISEASES USING DEEP NEURAL NETWORKS

Abstract

Diabetic eye diseases, particularly Diabetic Retinopathy (DR), are one of the leading causes of vision impairment and blindness worldwide. Early detection and timely treatment are crucial to prevent severe complications. However, manual diagnosis by ophthalmologists is time-consuming and requires significant expertise. This project proposes DEEPDIABETIC: An Identification System of Diabetic Eye Diseases using Deep Neural Networks, which leverages deep learning techniques to automatically detect and classify diabetic eye conditions from retinal images. The proposed system utilizes Convolutional Neural Networks (CNNs) to analyze fundus images and identify features associated with diabetic retinopathy, such as microaneurysms, hemorrhages, and exudates. Preprocessing techniques such as image normalization, noise reduction, and contrast enhancement are applied to improve image quality and model performance. The system is trained on labeled datasets to classify images into different stages of diabetic retinopathy, ranging from no DR to severe DR. The performance of the model is evaluated using metrics such as accuracy, precision, recall, F1-score, and sensitivity. Experimental results demonstrate that the deep learning model achieves high accuracy in detecting diabetic eye diseases, reducing dependency on manual diagnosis. The system can assist healthcare professionals in early diagnosis, enabling timely treatment and preventing vision loss. Overall, this project highlights the potential of deep learning in medical image analysis and contributes to improving healthcare accessibility and efficiency.