{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T18:46:11Z","timestamp":1785177971892,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T00:00:00Z","timestamp":1589846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2014that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones\u2014were acquired from 500 patients at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. Five hundred RF datasets (sized 256 \u00d7 256) for each DR stage and a total of 2500 (500 \u00d7 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\u2014histogram (H), wavelet (W), co-occurrence matrix (COM) and run-length matrix (RLM)\u2014were 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.<\/jats:p>","DOI":"10.3390\/e22050567","type":"journal-article","created":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T02:48:24Z","timestamp":1589942904000},"page":"567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9374-791X","authenticated-orcid":false,"given":"Aqib","family":"Ali","sequence":"first","affiliation":[{"name":"Department of Computer Science &amp; IT, The Islamia University of Bahawalpur, Bahawalpur 61300, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3503-6535","authenticated-orcid":false,"given":"Salman","family":"Qadri","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; IT, The Islamia University of Bahawalpur, Bahawalpur 61300, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5081-741X","authenticated-orcid":false,"given":"Wali","family":"Khan Mashwani","sequence":"additional","affiliation":[{"name":"Institute of Numerical Sciences, Kohat University of Sciences &amp; Technology, Kohat 26000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8773-4821","authenticated-orcid":false,"given":"Wiyada","family":"Kumam","sequence":"additional","affiliation":[{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5463-4581","authenticated-orcid":false,"given":"Poom","family":"Kumam","sequence":"additional","affiliation":[{"name":"Center of Excellence in Theoretical and Computational Science (TaCS-CoE) &amp; KMUTT Fixed Point Research Laboratory, Room SCL 802 Fixed Point Laboratory, Science Laboratory Building, Departments of Mathematics, Faculty of Science, King Mongkut\u2019s University of Technology Thonburi (KMUTT), 126 Pracha-Uthit Road, Bang Mod, Thrung Khru, Bangkok 10140, Thailand"},{"name":"Department of Medical Research, China Medical University Hospital, Taichung 40402, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0529-8187","authenticated-orcid":false,"given":"Samreen","family":"Naeem","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; IT, The Islamia University of Bahawalpur, Bahawalpur 61300, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7929-2912","authenticated-orcid":false,"given":"Atila","family":"Goktas","sequence":"additional","affiliation":[{"name":"Department of Statistics, Mugla S\u0131tk\u0131 Ko\u00e7man University, Mugla 48000, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6192-9890","authenticated-orcid":false,"given":"Farrukh","family":"Jamal","sequence":"additional","affiliation":[{"name":"Department of Statistics, Govt S.A Post Graduate College Dera Nawab Sahib, Bahawalpur 63351, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christophe","family":"Chesneau","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Universit\u00e9 de Caen, LMNO, Campus II, Science 3, 14032 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sania","family":"Anam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Govt Degree College for Women Ahmadpur East, Bahawalpur 63350, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4040-6211","authenticated-orcid":false,"given":"Muhammad","family":"Sulaiman","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"93751","DOI":"10.1172\/jci.insight.93751","article-title":"Diabetic retinopathy: Current understanding, mechanisms, and treatment strategies","volume":"2","author":"Duh","year":"2017","journal-title":"Jci Insight"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"E2655","DOI":"10.1073\/pnas.1522014113","article-title":"Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus","volume":"113","author":"Sohn","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s40662-015-0026-2","article-title":"Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss","volume":"2","author":"Lee","year":"2015","journal-title":"Eye Vis."},{"key":"ref_4","first-page":"11","article-title":"Retinal image analysis: A review","volume":"2","author":"Nirmala","year":"2011","journal-title":"Int. J. Comput. Commun. Technol."},{"key":"ref_5","unstructured":"Pietsch, P. (1981). The Quest of Hologramic Mind, Houghton Mifflin Harcourt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s10916-007-9113-9","article-title":"Automated identification of diabetic retinopathy stages using digital fundus images","volume":"32","author":"Nayak","year":"2008","journal-title":"J. Med. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jitpakdee, P., Pakinee, A., and Bunyarit, U. (2012, January 16\u201318). A survey on hemorrhage detection in diabetic retinopathy retinal images. Proceedings of the 2012 9th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Phetchaburi, Thailand.","DOI":"10.1109\/ECTICon.2012.6254356"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1109\/TBME.2012.2193126","article-title":"An ensemble-based system for microaneurysm detection and diabetic retinopathy grading","volume":"59","author":"Antal","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4446","DOI":"10.1109\/TIP.2017.2710620","article-title":"Deepfix: A fully convolutional neural network for predicting human eye fixations","volume":"26","author":"Kruthiventi","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.artmed.2019.03.009","article-title":"A data-driven approach to referable diabetic retinopathy detection","volume":"96","author":"Pires","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.knosys.2019.03.016","article-title":"Automated identification and grading system of diabetic retinopathy using deep neural networks","volume":"175","author":"Zhang","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Harun, N.H., Yusof, Y., Hassan, F., and Embong, Z. (2019, January 9\u201311). Classification of fundus images for diabetic retinopathy using artificial neural network. Proceedings of the 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan.","DOI":"10.1109\/JEEIT.2019.8717479"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"651","DOI":"10.2337\/dc18-0148","article-title":"Diagnostic accuracy of a device for the automated detection of diabetic retinopathy in a primary care setting","volume":"42","author":"Verbraak","year":"2019","journal-title":"Diabetes Care"},{"key":"ref_14","unstructured":"Rubya, A., and Shill, P.C. (2019, January 10\u201312). Automatic lesions detection and classification of diabetic retinopathy using fuzzy logic. Proceedings of the 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ricky, P., Lakshmanan, R., Purushotham, S., and Soundrapandiyan, R. (2019). Detecting Diabetic Retinopathy from Retinal Images Using CUDA Deep Neural Network. Intell. Pervasive Comput. Syst. Smarter Healthc., 379\u2013396.","DOI":"10.1002\/9781119439004.ch17"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2054","DOI":"10.3390\/molecules22122054","article-title":"Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image","volume":"22","author":"Kele","year":"2017","journal-title":"Molecules"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s11892-015-0577-6","article-title":"Automated retinal image analysis for diabetic retinopathy in telemedicine","volume":"15","author":"Sim","year":"2015","journal-title":"Curr. Diabetes Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Rishab","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_20","unstructured":"Quaid-e-Azam Medical College (2019, February 22). Bahawal Victoria Hospital\u2014Quaid-e-Azam Medical College. Available online: https:\/\/www.qamc.edu.pk\/bahawalvictoriahospital\/."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, J., Wang, W., and Lin, C. (2017). A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors, 17.","DOI":"10.3390\/s17071474"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, G., Gao, Z., Zhang, Y., and Ma, B. (2018). Adaptive maximum correntropy Gaussian filter based on variational Bayes. Sensors, 18.","DOI":"10.3390\/s18061960"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9248","DOI":"10.3390\/s130709248","article-title":"Retinal identification based on an improved circular gabor filter and scale invariant feature transform","volume":"13","author":"Meng","year":"2013","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guo, Y., Ashour, A.S., and Smarandache, F. (2018). A novel skin lesion detection approach using neutrosophic clustering and adaptive region growing in dermoscopy images. Symmetry, 10.","DOI":"10.3390\/sym10040119"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"16128","DOI":"10.3390\/s140916128","article-title":"White blood cell segmentation by color-space-based k-means clustering","volume":"14","author":"Zhang","year":"2014","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1109\/83.730379","article-title":"Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications","volume":"7","author":"Chen","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","unstructured":"Ng, H.P., Ong, S.H., Foong, K.W.C., Goh, P.S., and Nowinski, W.L. (2006, January 26\u201328). Medical image segmentation using k-means clustering and improved watershed algorithm. Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation, Denver, CO, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S0146-664X(75)80008-6","article-title":"Texture analysis using gray level run lengths","volume":"4","author":"Galloway","year":"1975","journal-title":"Comput. Graph. Image Process"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/0167-8655(91)80014-2","article-title":"Image characterizations based on joint gray level\u2014Run length distributions","volume":"12","author":"Dasarathy","year":"1991","journal-title":"Pattern Recognit. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2018.2796600","article-title":"Histogram-based features selection and volume of interest ranking for brain PET image classification","volume":"6","author":"Garali","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2014.12.032","article-title":"Mammogram classification using two-dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer","volume":"154","author":"Beura","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_32","unstructured":"Kocio\u0142ek, M., Materka, A., Strzelecki, M., and Szczypi\u0144ski, P. (2001, January 18\u201321). Discrete wavelet transform-derived features for digital image texture analysis. Proceedings of the International Conference on Signals and Electronic Systems, Lodz, Poland."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mateen, M., Wen, J., Song, S., and Huang, Z. (2019). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11.","DOI":"10.3390\/sym11010001"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/3515418","article-title":"Multisource Data Fusion Framework for Land Use\/Land Cover Classification Using Machine Vision","volume":"2017","author":"Qadri","year":"2017","journal-title":"J. Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.nima.2012.09.006","article-title":"A software tool for automatic classification and segmentation of 2D\/3D medical images","volume":"702","author":"Strzelecki","year":"2013","journal-title":"Nucl. Instrum. Methods Phys. Res. A"},{"key":"ref_36","first-page":"1","article-title":"Correlation-Based Feature Selection for Machine Learning","volume":"Volume 1","author":"Hall","year":"1999","journal-title":"Ph.D. Thesis"},{"key":"ref_37","unstructured":"(2019, November 10). The Islamia University of Bahawalpur \u201cIUB\u201d Pakistan. Available online: https:\/\/www.iub.edu.pk\/."},{"key":"ref_38","unstructured":"(2019, January 11). High-Resolution Fundus (hrf) Image Database. Available online: https:\/\/www5.cs.fau.de\/research\/data\/fundus-images\/."},{"key":"ref_39","unstructured":"(2018, December 15). MESSIDOR-2. Digital Retinal Images, LaTIM Laboratory, France. Available online: http:\/\/latim.univ-brest.fr\/."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/5\/567\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:30:08Z","timestamp":1760175008000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/5\/567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,19]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["e22050567"],"URL":"https:\/\/doi.org\/10.3390\/e22050567","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,19]]}}}