Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy C...
Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR—that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones—were acquired from 500 p...
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Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image
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TN_cdi_doaj_primary_oai_doaj_org_article_82766d47288b4cbbb1a9fa74d003ecb3
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_82766d47288b4cbbb1a9fa74d003ecb3
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ISSN
1099-4300
E-ISSN
1099-4300
DOI
10.3390/e22050567
