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Open AccessArticle

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

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Department of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 61300, Pakistan
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Institute of Numerical Sciences, Kohat University of Sciences & Technology, Kohat 26000, Pakistan
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Program in Applied Statistics, Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi (RMUTT), Thanyaburi, Pathumthani 12110, Thailand
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Center of Excellence in Theoretical and Computational Science (TaCS-CoE) & KMUTT Fixed Point Research Laboratory, Room SCL 802 Fixed Point Laboratory, Science Laboratory Building, Departments of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), 126 Pracha-Uthit Road, Bang Mod, Thrung Khru, Bangkok 10140, Thailand
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Department of Medical Research, China Medical University Hospital, Taichung 40402, Taiwan
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Department of Statistics, Mugla Sıtkı Koçman University, Mugla 48000, Turkey
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Department of Statistics, Govt S.A Post Graduate College Dera Nawab Sahib, Bahawalpur 63351, Pakistan
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Department of Mathematics, Université de Caen, LMNO, Campus II, Science 3, 14032 Caen, France
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Department of Computer Science, Govt Degree College for Women Ahmadpur East, Bahawalpur 63350, Pakistan
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Department of Mathematics, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan
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Authors to whom correspondence should be addressed.
Entropy 2020, 22(5), 567; https://doi.org/10.3390/e22050567
Received: 23 March 2020 / Revised: 23 April 2020 / Accepted: 23 April 2020 / Published: 19 May 2020
(This article belongs to the Special Issue Information-Theoretic Data Mining)
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 patients at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. Five hundred RF datasets (sized 256 × 256) for each DR stage and a total of 2500 (500 × 5) datasets of the five DR stages were acquired. This research introduces the novel clustering-based automated region growing framework. For texture analysis, four types of features—histogram (H), wavelet (W), co-occurrence matrix (COM) and run-length matrix (RLM)—were extracted, and various ML classifiers were employed, achieving 77.67%, 80%, 89.87%, and 96.33% classification accuracies, respectively. To improve classification accuracy, a fused hybrid-feature dataset was generated by applying the data fusion approach. From each image, 245 pieces of hybrid feature data (H, W, COM, and RLM) were observed, while 13 optimized features were selected after applying four different feature selection techniques, namely Fisher, correlation-based feature selection, mutual information, and probability of error plus average correlation. Five ML classifiers named sequential minimal optimization (SMO), logistic (Lg), multi-layer perceptron (MLP), logistic model tree (LMT), and simple logistic (SLg) were deployed on selected optimized features (using 10-fold cross-validation), and they showed considerably high classification accuracies of 98.53%, 99%, 99.66%, 99.73%, and 99.73%, respectively. View Full-Text
Keywords: diabetic retinopathy; clustering; segmentation; hybrid features; classification diabetic retinopathy; clustering; segmentation; hybrid features; classification
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Ali, A.; Qadri, S.; Khan Mashwani, W.; Kumam, W.; Kumam, P.; Naeem, S.; Goktas, A.; Jamal, F.; Chesneau, C.; Anam, S.; Sulaiman, M. Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image. Entropy 2020, 22, 567.

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