Diabetic Retinopathy Detection using Deep Learning

Diabetic Retinopathy threatens vision in diabetics, necessitating swift and accurate detection. This study employs Convolutional Neural Network (CNN), ResNet50, and InceptionV3 for automatic DR identification, achieving a notable 96.18% accuracy over 80 epochs. To enhance robustness, a pre-processing pipeline incorporates Gaussian filtering, CLAHE, median filtering, and top-hat filtering, significantly advancing DR detection accuracy. Evaluation on the APTOS 2019 dataset (1299 training, 279 testing images) reveals great accuracy as well as sensitivity, and specificity, forming a basis for early intervention and vision impairment prevention. This research at the nexus of DL which is also known as deep learning and medical image analyze offers a promising solution for early DR detection. The 96.18% accuracy demonstrates practical viability, positioning our approach as a valuable tool for healthcare practitioners and ophthalmologists in effectively diagnosing and managing diabetic retinopathy.