Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image

Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image

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,...

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Bibliographic Details
Main Authors: Aqib Ali, Salman Qadri, Wali Khan Mashwani, Wiyada Kumam, Poom Kumam, Samreen Naeem, Atila Goktas, Farrukh Jamal, Christophe Chesneau, Sania Anam, Muhammad Sulaiman
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Entropy
Subjects:
diabetic retinopathy
clustering
segmentation
hybrid features
classification
Online Access:https://www.mdpi.com/1099-4300/22/5/567

Internet

https://www.mdpi.com/1099-4300/22/5/567

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