Diabetic Retinopathy Diagnosis with Deep Learning

Diabetic retinopathy (DR) is one of the leading causes of vision impairment and blindness among diabetic patients worldwide. Early detection and timely treatment are essential to prevent severe vision loss; however, manual screening of retinal fundus images is time-consuming, costly, and prone to human error. Recent advances in deep learning have enabled the development of automated systems for accurate and efficient diagnosis of diabetic retinopathy. a deep learning–based approach is proposed for the detection and classification of diabetic retinopathy using retinal fundus images. Convolutional Neural Networks (CNNs) are employed to automatically extract relevant features such as microaneurysms, hemorrhages, and exudates without the need for handcrafted feature extraction. The model is trained and evaluated on publicly available retinal image datasets after preprocessing steps including image resizing, normalization, and contrast enhancement. Experimental results demonstrate that the proposed deep learning model achieves high accuracy, sensitivity, and specificity in classifying different stages of diabetic retinopathy