A comprehensive study of machine learning methods on diabetic retinopathy classification

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dc.authorscopusid57209239500
dc.authorscopusid36018188000
dc.authorscopusid57202924825
dc.contributor.authorGürcan, Ömer Faruk
dc.contributor.authorBeyca, Ömer Faruk
dc.contributor.authorDoğan, Onur
dc.date.accessioned2022-02-15T16:57:48Z
dc.date.available2022-02-15T16:57:48Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractDiabetes is one of the emerging threats to public health all over the world. According to projections by the World Health Organization, diabetes will be the seventh foremost cause of death in 2030 (WHO, Diabetes, 2020. https://www.afro.who.int/healthtopics/diabetes). Diabetic retinopathy (DR) results from long-lasting diabetes and is the fifth leading cause of visual impairment, worldwide. Early diagnosis and treatment processes are critical to overcoming this disease. The diagnostic procedure is challenging, especially in low-resource settings, or time-consuming, depending on the ophthalmologist's experience. Recently, automated systems now address DR classification tasks. This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods. Features are extracted from a pretrained model by the transfer learning approach. DR images are classified by several machine learning methods. XGBoost outperforms other methods. Dimensionality reduction algorithms are applied to obtain a lower-dimensional representation of extracted features. The proposed model is trained and evaluated on a publicly available dataset. Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods. The proposed model offers a robust solution for detecting DR with a small number of images. We used a transfer learning approach, which differs from other studies in the literature, during the feature extraction. It provides a data-driven, cost-effective solution, which includes comprehensive preprocessing and fine-tuning processes. (C) 2021 The Authors. Published by Atlantis Press B.V.en_US
dc.identifier.doi10.2991/ijcis.d.210316.001
dc.identifier.endpage1141en_US
dc.identifier.issn1875-6891
dc.identifier.issn1875-6883
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85104639972en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1132en_US
dc.identifier.urihttps://doi.org/10.2991/ijcis.d.210316.001
dc.identifier.urihttps://hdl.handle.net/20.500.14034/279
dc.identifier.volume14en_US
dc.identifier.wosWOS:000657692400001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAtlantis Pressen_US
dc.relation.journalInternational Journal Of Computational Intelligence Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmachine learningen_US
dc.subjectensemble learningen_US
dc.subjecttransfer learningen_US
dc.subjectXGBoosten_US
dc.subjectPCAen_US
dc.subjectSVDen_US
dc.subjectImagesen_US
dc.titleA comprehensive study of machine learning methods on diabetic retinopathy classificationen_US
dc.typeArticleen_US

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