GitHub / 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.
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PURL: pkg:github/SuryaVamsi-P/Diabetic-Retinopathy-Detection-with-ResNet50
Stars: 0
Forks: 0
Open issues: 0
License: None
Language: Python
Size: 2.15 MB
Dependencies parsed at: Pending
Created at: almost 2 years ago
Updated at: 2 months ago
Pushed at: 2 months ago
Last synced at: 19 days ago
Commit Stats
Commits: 6
Authors: 1
Mean commits per author: 6.0
Development Distribution Score: 0.0
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Topics: 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