{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:00:23Z","timestamp":1777611623326,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to choose a robust machine learning (ML) model, usually used for data classification. AI has a large connection with image processing and feature classification, and it can also be successfully applied to analyzing fundus eye images. Diabetic retinopathy (DR) is a disease that can cause vision loss and blindness, which, from an imaging point of view, can be shown when screening the eyes. Image processing tools can analyze and extract the features from fundus eye images, and these corroborate with ML classifiers that can perform their classification among different disease classes. The outcomes integrated into automated diagnostic systems can be a real success for physicians and patients. In this study, in the form image processing area, the manipulation of the contrast with the gamma correction parameter was applied because DR affects the blood vessels, and the structure of the eyes becomes disorderly. Therefore, the analysis of the texture with two types of entropies was necessary. Shannon and fuzzy entropies and contrast manipulation led to ten original features used in the classification process. The machine learning library PyCaret performs complex tasks, and the empirical process shows that of the fifteen classifiers, the gradient boosting classifier (GBC) provides the best results. Indeed, the proposed model can classify the DR degrees as normal or severe, achieving an accuracy of 0.929, an F1 score of 0.902, and an area under the curve (AUC) of 0.941. The validation of the selected model with a bootstrap statistical technique was performed. The novelty of the study consists of the extraction of features from preprocessed fundus eye images, their classification, and the manipulation of the contrast in a controlled way.<\/jats:p>","DOI":"10.3390\/jimaging10010008","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T09:56:27Z","timestamp":1703757387000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Robust Machine Learning Model for Diabetic Retinopathy Classification"],"prefix":"10.3390","volume":"10","author":[{"given":"Gigi","family":"T\u0103b\u0103caru","sequence":"first","affiliation":[{"name":"Department of Automatic Control and Electrical Engineering, Faculty of Automation, Computers, Electrical, Engineering and Electronics, \u201cDunarea de Jos\u201d University of Galati, 800008 Gala\u021bi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5934-329X","authenticated-orcid":false,"given":"Simona","family":"Moldovanu","sequence":"additional","affiliation":[{"name":"Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, \u201cDunarea de Jos\u201d University of Galati, 800210 Galati, Romania"},{"name":"The Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"R\u0103ducan","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Electrical Engineering, Faculty of Automation, Computers, Electrical, Engineering and Electronics, \u201cDunarea de Jos\u201d University of Galati, 800008 Gala\u021bi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6645-3705","authenticated-orcid":false,"given":"Marian","family":"Barbu","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Electrical Engineering, Faculty of Automation, Computers, Electrical, Engineering and Electronics, \u201cDunarea de Jos\u201d University of Galati, 800008 Gala\u021bi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"150530","DOI":"10.1109\/ACCESS.2019.2947484","article-title":"A deep learning ensemble approach for diabetic retinopathy detection","volume":"7","author":"Qummar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107840","DOI":"10.1016\/j.diabres.2019.107840","article-title":"IDF Diabetes Atlas: A review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018","volume":"157","author":"Thomas","year":"2019","journal-title":"Diabetes Res. 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