https://github.com/suryavamsi-p/diabetic-retinopathy-detection-with-resnet50

Built an end-to-end deep learning pipeline using ResNet-50 to classify retinal images into five stages of Diabetic Retinopathy. Applied transfer learning, image preprocessing, and AUC-based evaluation on the APTOS 2019 Kaggle dataset, achieving a 94% validation AUC—offering real-world potential in clinical diagnosis automation.

aptos2019 auc-evaluation clinical-decision-support cnn-model computer-vision data-augmentation deep-learning diabetic-retinopathy early-detection healthcare-ai image-classification keras medical-diagnosis medical-imaging multi-class-classification optical-imaging resnet50 retinal-image-analysis tensorflow transfer-learning

Last synced: 13 Apr 2026