{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T03:01:14Z","timestamp":1790564474324,"version":"4.1.0"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Diabetic retinopathy (DR) remains one of the world\u2019s frequent eye illnesses, leading to vision loss among working-aged individuals. Hemorrhages and exudates are examples of signs of DR. However, artificial intelligence (AI), particularly deep learning (DL), is poised to impact nearly every aspect of human life and gradually transform medical practice. Insight into the condition of the retina is becoming more accessible thanks to major advancements in diagnostic technology. AI approaches can be used to assess lots of morphological datasets derived from digital images in a rapid and noninvasive manner. Computer-aided diagnosis tools for automatic detection of DR early-stage signs will ease the pressure on clinicians. In this work, we apply two methods to the color fundus images taken on-site at the Cheikh Za\u00efd Foundation\u2019s Ophthalmic Center in Rabat to detect both exudates and hemorrhages. First, we apply the U-Net method to segment exudates and hemorrhages into red and green colors, respectively. Second, the You Look Only Once Version 5 (YOLOv5) method identifies the presence of hemorrhages and exudates in an image and predicts a probability for each bounding box. The segmentation proposed method obtained a specificity of 85%, a sensitivity of 85%, and a Dice score of 85%. The detection software successfully detected 100% of diabetic retinopathy signs, the expert doctor detected 99% of DR signs, and the resident doctor detected 84%.<\/jats:p>","DOI":"10.3390\/diagnostics13101694","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T01:37:09Z","timestamp":1683769029000},"page":"1694","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion"],"prefix":"10.3390","volume":"13","author":[{"given":"Zineb","family":"Farahat","sequence":"first","affiliation":[{"name":"LISTD Laboratory, Ecole Nationale Sup\u00e9rieure des Mines de Rabat, Rabat 10000, Morocco"},{"name":"Cheikh Za\u00efd Foundation Medical Simulation Center, Rabat 10000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nabila","family":"Zrira","sequence":"additional","affiliation":[{"name":"LISTD Laboratory, Ecole Nationale Sup\u00e9rieure des Mines de Rabat, Rabat 10000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5593-4674","authenticated-orcid":false,"given":"Nissrine","family":"Souissi","sequence":"additional","affiliation":[{"name":"LISTD Laboratory, Ecole Nationale Sup\u00e9rieure des Mines de Rabat, Rabat 10000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Safia","family":"Benamar","sequence":"additional","affiliation":[{"name":"Cheikh Za\u00efd Ophthalmic Center, Cheikh Za\u00efd International University Hospital, Rabat 10000, Morocco"},{"name":"Institut Sup\u00e9rieur d\u2019Ing\u00e9nierie et Technologies de Sant\u00e9\/Facult\u00e9 de M\u00e9decine Abulcasis, Universit\u00e9 Internationale Abulcasis des Sciences de la Sant\u00e9, Rabat 10000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Belmekki","sequence":"additional","affiliation":[{"name":"Cheikh Za\u00efd Ophthalmic Center, Cheikh Za\u00efd International University Hospital, Rabat 10000, Morocco"},{"name":"Institut Sup\u00e9rieur d\u2019Ing\u00e9nierie et Technologies de Sant\u00e9\/Facult\u00e9 de M\u00e9decine Abulcasis, Universit\u00e9 Internationale Abulcasis des Sciences de la Sant\u00e9, Rabat 10000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed Nabil","family":"Ngote","sequence":"additional","affiliation":[{"name":"LISTD Laboratory, Ecole Nationale Sup\u00e9rieure des Mines de Rabat, Rabat 10000, Morocco"},{"name":"Institut Sup\u00e9rieur d\u2019Ing\u00e9nierie et Technologies de Sant\u00e9\/Facult\u00e9 de M\u00e9decine Abulcasis, Universit\u00e9 Internationale Abulcasis des Sciences de la Sant\u00e9, Rabat 10000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kawtar","family":"Megdiche","sequence":"additional","affiliation":[{"name":"Cheikh Za\u00efd Foundation Medical Simulation Center, Rabat 10000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"428","DOI":"10.4103\/0301-4738.100542","article-title":"The worldwide epidemic of diabetic retinopathy","volume":"60","author":"Zheng","year":"2012","journal-title":"Indian J. Ophthalmol."},{"key":"ref_2","first-page":"58","article-title":"Aspects epidemiologiques de la r\u00e9tinopathie diab\u00e9tique \u00e0 casablanca","volume":"28","author":"Razzak","year":"2019","journal-title":"J. De La Soci\u00e9t\u00e9 Maroc. D\u2019ophtalmologie"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farahat, Z., Souissi, N., Belmekki, M., Megdiche, K., Benamar, S., Bennani, Y., Bencherif, S., and Ngote, N. (2021, January 20\u201322). Diabetic retinopathy: New perspectives with artificial intelligence. Proceedings of the 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), Fez, Morocco.","DOI":"10.1109\/ICDS53782.2021.9626762"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Benamar, S., Bennani, Y., Bencherif, S., Farahat, Z., Souissi, N., Ngote, N., Megdiche, K., and Belmekki, M. (2021, January 20\u201322). Diabetic retinopathy screening and management in morocco: Challenges and possible solutions. Proceedings of the 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), Fez, Morocco.","DOI":"10.1109\/ICDS53782.2021.9626731"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1586\/17469899.2014.945521","article-title":"Diabetic retinopathy: Current and future methods for early screening from a retinal hemodynamic and geometric approach","volume":"9","author":"Leontidis","year":"2014","journal-title":"Expert Rev. Ophthalmol."},{"key":"ref_6","first-page":"6","article-title":"Hemodynamics in the retinal vasculature during the progression of diabetic retinopathy","volume":"1","author":"Leontidis","year":"2017","journal-title":"J. Model. Ophthalmol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101799","DOI":"10.1016\/j.redox.2020.101799","article-title":"Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications","volume":"37","author":"Kang","year":"2020","journal-title":"Redox Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1111\/dme.12089","article-title":"Diabetic retinopathy: Pathogenesis, clinical grading, management and future developments","volume":"30","author":"Heng","year":"2013","journal-title":"Diabet. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1007\/s00125-022-05727-0","article-title":"Diabetic retinopathy screening in the emerging era of artificial intelligence","volume":"65","author":"Grauslund","year":"2022","journal-title":"Diabetologia"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s00417-021-05511-7","article-title":"Analysis of international publication trends in artificial intelligence in ophthalmology","volume":"260","author":"Boudry","year":"2022","journal-title":"Graefe\u2019s Arch. Clin. Exp. Ophthalmol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., and Meriaudeau, F. (2018). Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research. Data, 3.","DOI":"10.3390\/data3030025"},{"key":"ref_12","first-page":"6644071","article-title":"Ffu-net: Feature fusion u-net for lesion segmentation of diabetic retinopathy","volume":"2021","author":"Xu","year":"2021","journal-title":"BioMed Res. Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"185514","DOI":"10.1109\/ACCESS.2020.3029117","article-title":"An enhanced residual u-net for microa- neurysms and exudates segmentation in fundus images","volume":"8","author":"Kou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, Y., Wan, C., Shen, J., Chen, Z., Ye, H., and Yu, Q. (2020, January 20\u201324). Mau-net: A retinal vessels segmentation method. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176093"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105395","DOI":"10.1016\/j.cmpb.2020.105395","article-title":"Dense-inception u-net for medical image segmentation","volume":"192","author":"Zhang","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3242","DOI":"10.1038\/s41467-021-23458-5","article-title":"A deep learning system for detecting diabetic retinopathy across the disease spectrum","volume":"12","author":"Dai","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1016\/j.bbe.2020.05.006","article-title":"Modified U-Net architecture for semantic segmentation of diabetic retinopathy images","volume":"40","author":"Sambyal","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1002\/ima.22720","article-title":"A deep data-driven approach for enhanced segmentation of blood vessel for diabetic retinopathy","volume":"32","author":"Yadav","year":"2020","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Soomro, T.A., Hellwich, O., Afifi, A.J., Paul, M., Gao, J., and Zheng, L. (2018, January 10\u201313). Strided U-Net model: Retinal vessels segmentation using dice loss. Proceedings of the Digital Image Computing: Techniques and Applications, Canberra, ACT, Australia.","DOI":"10.1109\/DICTA.2018.8615770"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102785","DOI":"10.1016\/j.bspc.2021.102785","article-title":"A novel retinal image segmentation using rSVM boosted convolutional neural network for exudates detection","volume":"68","author":"Ghosh","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.2337\/dc20-1877","article-title":"Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems","volume":"44","author":"Lee","year":"2021","journal-title":"Diabetes Care"},{"key":"ref_22","first-page":"49","article-title":"Detection of red lesions in retinal fundus images using yolo v3","volume":"7","author":"Pal","year":"2020","journal-title":"Curr. Indian Eye Res. J. Ophthalmic Res. Group"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s13534-019-00136-6","article-title":"Film: Finding the location of microaneurysms on the retina","volume":"9","author":"Akut","year":"2019","journal-title":"Biomed. Eng. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1007\/s10792-020-01685-x","article-title":"Evaluation of a novel artificial intelligence-based screening system for dia- betic retinopathy in community of china: A real-world study","volume":"41","author":"Ming","year":"2021","journal-title":"Int. Ophthalmol."},{"key":"ref_25","first-page":"669","article-title":"Validation and application of an artificial intelligence robot assisted diag- nosis system for diabetic retinopathy","volume":"12","author":"Gao","year":"2019","journal-title":"Chin. J. Exp. Ophthalmol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11407","DOI":"10.1109\/TCYB.2021.3062638","article-title":"Robust collaborative learning of patch-level and image-level annotations for diabetic retinopathy grading from fundus image","volume":"52","author":"Yang","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a pub-licly distributed image database: The messidor database","volume":"33","author":"Zhang","year":"2014","journal-title":"Image Anal. Stereol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1177\/193229680900300315","article-title":"Eyepacs: An adaptable telemedicine system for diabetic retinopathy screening","volume":"3","author":"Cuadros","year":"2009","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","article-title":"Artificial intelligence in retina","volume":"67","author":"Sadeghipour","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pan, C., Li, X., and Yan, W.Q. (2018, January 19\u201321). A learning-based positive feedback approach in salient object detection. Proceedings of the 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealand.","DOI":"10.1109\/IVCNZ.2018.8634717"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.crfs.2021.10.003","article-title":"Detection of mold on the food surface using yolov5","volume":"4","author":"Jubayer","year":"2021","journal-title":"Curr. Res. Food Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, L., and Yan, W.Q. (2021, January 28\u201329). Tree leaves detection based on deep learning. Proceedings of the Geometry and Vision: First International Symposium, ISGV 2021, Auckland, New Zealand. Revised Selected Papers 1.","DOI":"10.1007\/978-3-030-72073-5_3"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/10\/1694\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:31Z","timestamp":1760124751000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/10\/1694"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["diagnostics13101694"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13101694","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}