{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:28:26Z","timestamp":1783164506622,"version":"3.54.6"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be validated before choosing the best treatment. In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based feature extraction. Interconnected layers helps to identify the critical features or characteristics; in addition, Ant Colony systems also help choose the characteristics. Passing these chosen attributes through SVM with multiple kernels yielded the final classification model with promising accuracy. The experiment based on 750 features proves that the proposed approach has achieved an accuracy of 93%.<\/jats:p>","DOI":"10.3390\/info14010030","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T02:00:57Z","timestamp":1672884057000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Abdul Muiz","family":"Fayyaz","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Wah, Wah 47040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4786-6579","authenticated-orcid":false,"given":"Muhammad Imran","family":"Sharif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Wah 47040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7572-9750","authenticated-orcid":false,"given":"Sami","family":"Azam","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Charles Darwin University, Darwin 0909, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8532-6816","authenticated-orcid":false,"given":"Asif","family":"Karim","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Charles Darwin University, Darwin 0909, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamal","family":"El-Den","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Charles Darwin University, Darwin 0909, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s42600-022-00233-z","article-title":"Classification of diabetic macular edema severity using deep learning technique","volume":"38","author":"Kumar","year":"2022","journal-title":"Res. 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