GitHub / shraddha-sanil / diabetic-retinopathy-detection-methodological-framework
This DR detection methodology has six steps: preprocessing, segmentation of blood vessels, segmentation of OD, detection of MAs and hemorrhages, feature extraction and classification. For segmentation of blood vessels BCDU-Net is used. For OD segmentation, U-Net model is used. MAs and hemorrhages are extracted using Otsu thresholding technique. Both clinical and non-clinical features are extracted and fed to SVM classifier.
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PURL: pkg:github/shraddha-sanil/diabetic-retinopathy-detection-methodological-framework
Stars: 3
Forks: 3
Open issues: 0
License: None
Language: Jupyter Notebook
Size: 3.5 MB
Dependencies parsed at: Pending
Created at: almost 5 years ago
Updated at: almost 2 years ago
Pushed at: almost 5 years ago
Last synced at: almost 2 years ago
Topics: blood-vessel-segmentation, blood-vessels-extraction, circular-hough-transform, clahe, classification, diabetic-retinopathy, diabetic-retinopathy-detection, feature-extraction, glcm, image-preprocessing, image-processing, matlab, optic-disc-segmentation, otsu-thresholding, python, segmentation, svm, svm-classifier, svm-linear