An Optimized Deep Learning Based Technique for Grading and Extraction of Diabetic Retinopathy Severities

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dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.contributor.authorZhang, Qiu-Ming
dc.contributor.authorLuo, Jing
dc.contributor.authorCengiz, Korhan
dc.date.accessioned2024-06-12T10:59:28Z
dc.date.available2024-06-12T10:59:28Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThe prognosis of Diabetic Retinopathy (DR) requires regular eye examinations, as ophthalmologists depends on fundus segmentation to treat DR pathologies. Automated approaches for detection, segmentation and classification have developed as an imperative area of research for the effective diagnosis of DR for the treatment of serious eye conditions that prevent visual impairment. Diagnosis of various DR lesions, as well as different severities, helping the ophthalmologists to analyze variations in fundus images and take the necessary measures before the disease progresses. Deep learning techniques have evolved as a recent advent to combat the issues of conventional machine leaning based methods. An optimized deep learning framework is proposed in this article for grading and extraction of diabetic retinopathy severities. This involves various steps like background segmentation, feature set extraction, feature optimization using Cuckoo search and Convolutional Neural Network (CNN) severity grade classification. The method was validated on two standard datasets MESSIDOR and IDRiD. The proposed method yields an accuracy value of 97.55%, cross entropy loss of 0.367 and time intricacy of 20 mins and 15 secs for MESSIDOR and 98.02% cross entropy loss of 0.345 and time intricacy of 22 mins and 21 secs for IDRiD dataset; respectively. The state-of-the-art comparison depicts that the proposed CNN based method provides a maximum accuracy improvement of 10.46% comparative to the existing methodology. The proposed framework yields better accuracy by procurement of the investigative outcomes acquired exhibits proficient DR determination.en_US
dc.identifier.doi10.31449/inf.v45i5.3561
dc.identifier.endpage665en_US
dc.identifier.issn0350-5596
dc.identifier.issn1854-3871
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85112317262en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage659en_US
dc.identifier.urihttps://doi.org/10.31449/inf.v45i5.3561
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20463
dc.identifier.volume45en_US
dc.identifier.wosWOS:000752206700009en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSlovensko Drustvo Informatikaen_US
dc.relation.ispartofInformatica-An International Journal Of Computing And Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDiabetic Retinopathyen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectConvolution Neural Networken_US
dc.subjectSeverity Gradingen_US
dc.subjectDiagnostic Systemen_US
dc.titleAn Optimized Deep Learning Based Technique for Grading and Extraction of Diabetic Retinopathy Severitiesen_US
dc.typeArticleen_US

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