Diabetic Retinopathy Detection using Deep Learning
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Abstract
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.
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