{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T21:26:42Z","timestamp":1771104402173,"version":"3.50.1"},"reference-count":55,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Diabetes is one of the most prevalent diseases in the world, which is a metabolic disorder characterized by high blood sugar. Diabetes complications are leading to Diabetic Retinopathy (DR). The early stages of DR may have either no sign or cause minor vision problems, but later stages of the disease can lead to blindness. DR diagnosis is an exceedingly difficult task because of changes in the retina during the disease stages. An automatic DR early detection method can save a patient's vision and can also support the ophthalmologists in DR screening. This paper develops a model for the diagnostics of DR. Initially, we extract and fuse the ophthalmoscopic features from the retina images based on textural gray-level features like co-occurrence, run-length matrix, as well as the coefficients of the Ridgelet Transform. Based on the retina features, the Sequential Minimal Optimization (SMO) classification is used to classify diabetic retinopathy. For performance analysis, the openly accessible retinal image datasets are used, and the findings of the experiments demonstrate the quality and efficacy of the proposed method (we achieved 98.87% sensitivity, 95.24% specificity, 97.05% accuracy on DIARETDB1 dataset, and 90.9% sensitivity, 91.0% specificity, 91.0% accuracy on KAGGLE dataset).<\/jats:p>","DOI":"10.7717\/peerj-cs.456","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T10:29:04Z","timestamp":1620383344000},"page":"e456","source":"Crossref","is-referenced-by-count":68,"title":["Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier"],"prefix":"10.7717","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0643-6599","authenticated-orcid":true,"given":"Lakshmana Kumar","family":"Ramasamy","sequence":"first","affiliation":[{"name":"Hindusthan College of Engineering and Technology, Coimbatore, India"}]},{"given":"Shynu Gopalan","family":"Padinjappurathu","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology University, Vellore, India"}]},{"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[{"name":"Noroff University College, Kristiansand, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":true,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania"}]}],"member":"4443","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"issue":"11","key":"10.7717\/peerj-cs.456\/ref-1","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1007\/s11517-017-1638-6","article-title":"Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features","volume":"55","author":"Abbas","year":"2017","journal-title":"Medical and 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