{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:58:10Z","timestamp":1774965490780,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research DSR, King Abdulaziz University, Jeddah, Saudi Arabia","award":["KEP-Msc-37-135-38"],"award-info":[{"award-number":["KEP-Msc-37-135-38"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include \u2019No Referable Diabetic Macular Edema Grade (DME)\u2019 and \u2019Referable DME\u2019 while five categories consist of \u2018Proliferative diabetic retinopathy\u2019, \u2018Severe\u2019, \u2018Moderate\u2019, \u2018Mild\u2019, and \u2018No diabetic retinopathy\u2019. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.<\/jats:p>","DOI":"10.3390\/s21113883","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T01:56:40Z","timestamp":1623031000000},"page":"3883","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection"],"prefix":"10.3390","volume":"21","author":[{"given":"Muhammad Kashif","family":"Yaqoob","sequence":"first","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, UMT Road, C-II Johar Town, Lahore 54782, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3943-903X","authenticated-orcid":false,"given":"Syed Farooq","family":"Ali","sequence":"additional","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, UMT Road, C-II Johar Town, Lahore 54782, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6446-8687","authenticated-orcid":false,"given":"Muhammad","family":"Bilal","sequence":"additional","affiliation":[{"name":"Center of Excellence in Intelligent Engineering Systems, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6316-9677","authenticated-orcid":false,"given":"Muhammad Shehzad","family":"Hanif","sequence":"additional","affiliation":[{"name":"Center of Excellence in Intelligent Engineering Systems, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2925-5184","authenticated-orcid":false,"given":"Ubaid M.","family":"Al-Saggaf","sequence":"additional","affiliation":[{"name":"Center of Excellence in Intelligent Engineering Systems, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, X., Lu, Y., Wang, Y., and Chen, W.B. 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