{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T19:30:17Z","timestamp":1772134217631,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely \u201cE-Ophtha\u201d and \u201cDIARETDB1\u201d, and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.<\/jats:p>","DOI":"10.3390\/s22020542","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:33:04Z","timestamp":1641933184000},"page":"542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Deep Learning Approach for Automatic Microaneurysms Detection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2282-5703","authenticated-orcid":false,"given":"Muhammad","family":"Mateen","sequence":"first","affiliation":[{"name":"Department of Computer Science, Air University Multan Campus, Multan 60000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2064-807X","authenticated-orcid":false,"given":"Tauqeer Safdar","family":"Malik","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Air University Multan Campus, Multan 60000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaukat","family":"Hayat","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3906-201X","authenticated-orcid":false,"given":"Musab","family":"Hameed","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhao","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.4103\/0970-0218.91324","article-title":"Role of early screening for diabetic retinopathy in patients with diabetes mellitus: An overview","volume":"36","author":"Vashist","year":"2011","journal-title":"Indian J. 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