{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:12:47Z","timestamp":1783570367997,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"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>Diabetic retinopathy (DR) is the main ocular complication of diabetes. Asymptomatic for a long time, it is subject to annual screening using dilated fundus or retinal photography to look for early signs. Fundus photography and optical coherence tomography (OCT) are used by ophthalmologists to assess retinal thickness and structure, as well as detect edema, hemorrhage, and scarring. The effectiveness of ConvNet no longer needs to be demonstrated, and its use in the field of imaging has made it possible to overcome many barriers, which were until now insurmountable with old methods. Throughout this study, a robust and optimal deep ConvNet is proposed to analyze fundus images and automatically distinguish between healthy, moderate, and severe DR. The proposed model combines the use of the ConvNet architecture taken from ImageNet, data augmentation, class balancing, and transfer learning in order to establish a benchmarking test. A significant improvement at the level of middle class which corresponds to the early stage of DR, which was the major problem in previous studies. By eliminating the need for retina specialists and broadening access to retinal care, the proposed model is substantially more robust in objectively early staging and detecting DR.<\/jats:p>","DOI":"10.3390\/info16030221","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T06:53:00Z","timestamp":1741848780000},"page":"221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0417-6586","authenticated-orcid":false,"given":"Minyar","family":"Sassi Hidri","sequence":"first","affiliation":[{"name":"Department of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0222-8562","authenticated-orcid":false,"given":"Adel","family":"Hidri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4699-6432","authenticated-orcid":false,"given":"Suleiman Ali","family":"Alsaif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4461-8446","authenticated-orcid":false,"given":"Muteeb","family":"Alahmari","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3288-4271","authenticated-orcid":false,"given":"Eman","family":"AlShehri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nguyen, Q.H., Muthuraman, R., Singh, L., Sen, G., Tran, A.C., Nguyen, B.P., and Chua, M. (2020, January 17\u201319). Diabetic Retinopathy Detection Using Deep Learning. Proceedings of the 4th International Conference on Machine Learning and Soft Computing, Haiphong, Vietnam.","DOI":"10.1145\/3380688.3380709"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Joshi, S., Kumar, R., Rai, P.K., and Garg, S. (2023, January 28\u201330). Diabetic Retinopathy Using Deep Learning. Proceedings of the 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India.","DOI":"10.1109\/CISES58720.2023.10183562"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wahab Sait, A.R. (2023). A Lightweight Diabetic Retinopathy Detection Model Using a Deep-Learning Technique. Diagnostics, 13.","DOI":"10.3390\/diagnostics13193120"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alwakid, G., Gouda, W., and Humayun, M. (2023). Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement. Healthcare, 11.","DOI":"10.20944\/preprints202302.0097.v1"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ten Dam, W., Grol, M., Zeegers, Z., Dehghani, A., and Aldewereld, H. (2023, January 14\u201315). Representative Data Generation of Diabetic Retinopathy Synthetic Retinal Images. Proceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice, Dublin, Ireland.","DOI":"10.1145\/3633083.3633175"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, K., Feng, D., and Mi, H. (2017). Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Molecules, 22.","DOI":"10.3390\/molecules22122054"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated Identification of Diabetic Retinopathy Using Deep Learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_8","first-page":"14","article-title":"Early diagnosis of diabetic retinopathy in primary care","volume":"46","year":"2015","journal-title":"Colomb. M\u00e9dica"},{"key":"ref_9","first-page":"635","article-title":"Detection of Diabetic Retinopathy Based on a Convolutional Neural Network Using Retinal Fundus Images","volume":"Volume 10614","author":"Lintas","year":"2017","journal-title":"Proceedings of the Artificial Neural Networks and Machine Learning \u2014ICANN 2017\u201426th International Conference on Artificial Neural Networks"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Firke, S.N., and Jain, R.B. (2021, January 25\u201327). Convolutional Neural Network for Diabetic Retinopathy Detection. Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India.","DOI":"10.1109\/ICAIS50930.2021.9395796"},{"key":"ref_11","first-page":"1","article-title":"Detection of Diabetic Retinopathy Based on Convolutional Neural Networks: A Review","volume":"8","author":"Rashid","year":"2021","journal-title":"Asian J. Res. Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez, C., Porta, M., Bandello, F., Grauslund, J., Harding, S.P., Aldington, S.J., Egan, C., Frydkjaer-Olsen, U., Garc\u00eda-Arum\u00ed, J., and Gibson, J. (2020). The Usefulness of Serum Biomarkers in the Early Stages of Diabetic Retinopathy: Results of the EUROCONDOR Clinical Trial. J. Clin. Med., 9.","DOI":"10.3390\/jcm9041233"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101206","DOI":"10.1016\/j.preteyeres.2023.101206","article-title":"Optical coherence tomography angiography in diabetic retinopathy","volume":"97","author":"Waheed","year":"2023","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.ophtha.2010.03.046","article-title":"Automated Early Detection of Diabetic Retinopathy","volume":"117","author":"Reinhardt","year":"2010","journal-title":"Ophthalmology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9093","DOI":"10.1007\/s00500-023-08418-z","article-title":"Early diagnosis of diabetic retinopathy using unsupervised learning","volume":"27","author":"Padmapriya","year":"2023","journal-title":"Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Z., Li, K., Mu, S., Zhou, X., and Di, Y. (2023). Performance of artificial intelligence in diabetic retinopathy screening: A systematic review and meta-analysis of prospective studies. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1197783"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Barakat, A.A., Mobarak, O., Javaid, H.A., Awad, M.R., Hamweyah, K., Ouban, A., and Al-Hazzaa, S.A.F. (2023). The application of artificial intelligence in diabetic retinopathy screening: A Saudi Arabian perspective. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1303300"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Poly, T.N., Islam, M.M., Walther, B.A., Lin, M.C., and Li, Y.J. (2023). Artificial intelligence in diabetic retinopathy: Bibliometric analysis. Comput. Methods Programs Biomed., 231.","DOI":"10.1016\/j.cmpb.2023.107358"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Noriega, A., Meizner, D., Camacho, D., Enciso, J., Quiroz-Mercado, H., Morales-Canton, V., Almaatouq, A., and Pentland, A. (2020). Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Mexico: Independent and Assistive use Cases. medRxiv.","DOI":"10.1101\/2020.07.20.20157859"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Das, D., Das, S., Biswas, S.K., and Purkayastha, B. (2021, January 27\u201329). Deep Diabetic Retinopathy Feature eXtraction and Random Forest based ensemble Classification System (DDRFXRFCS). Proceedings of the 2021 Asian Conference on Innovation in Technology (ASIANCON), Pune, India.","DOI":"10.1109\/ASIANCON51346.2021.9544899"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Uppamma, P., and Bhattacharya, S. (2023). A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: An ensemble learning approach. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-45886-7"},{"key":"ref_22","first-page":"78","article-title":"Diabetic retinopathy detection using principal component analysis multi-label feature extraction and classification","volume":"4","author":"Usman","year":"2023","journal-title":"Int. J. Cogn. Comput. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.procs.2023.08.181","article-title":"HDeep: Hierarchical Deep Learning Combination for Detection of Diabetic Retinopathy","volume":"222","author":"Camilo","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1049\/cvi2.12116","article-title":"Deep learning in the grading of diabetic retinopathy: A review","volume":"16","author":"Tajudin","year":"2022","journal-title":"IET Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shen, Z., Wu, Q., Wang, Z., Chen, G., and Lin, B. (2021). Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data. Sensors, 21.","DOI":"10.3390\/s21113663"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Reddy, G.T., Bhattacharya, S., Siva Ramakrishnan, S., Chowdhary, C.L., Hakak, S., Kaluri, R., and Praveen Kumar Reddy, M. (2020, January 24\u201325). An Ensemble based Machine Learning model for Diabetic Retinopathy Classification. Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India.","DOI":"10.1109\/ic-ETITE47903.2020.235"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102118","DOI":"10.1016\/j.media.2021.102118","article-title":"ExplAIn: Explanatory artificial intelligence for diabetic retinopathy diagnosis","volume":"72","author":"Quellec","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Obayya, M., Nemri, N., Nour, M.K., Al Duhayyim, M., Mohsen, H., Rizwanullah, M., Sarwar Zamani, A., and Motwakel, A. (2022). Explainable Artificial Intelligence Enabled TeleOphthalmology for Diabetic Retinopathy Grading and Classification. Appl. Sci., 12.","DOI":"10.3390\/app12178749"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3469841","article-title":"An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading","volume":"17","author":"Shorfuzzaman","year":"2021","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1167\/tvst.9.2.13","article-title":"Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology","volume":"9","author":"Lin","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gupta, S., Panwar, A., Kapruwan, A., Chaube, N., and Chauhan, M. (2022, January 12\u201313). Real Time Analysis of Diabetic Retinopathy Lesions by Employing Deep Learning and Machine Learning Algorithms using Color Fundus Data. Proceedings of the 2022 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India.","DOI":"10.1109\/ICITIIT54346.2022.9744228"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"E235","DOI":"10.1016\/S2589-7500(22)00017-6","article-title":"Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: A prospective interventional cohort study","volume":"4","author":"Ruamviboonsuk","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_36","unstructured":"Kumar, S. (2024, November 23). Diabetic Retinopathy Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/way2tutorials\/diabetic-retinopathy-dataset-2023."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"012049","DOI":"10.1088\/1742-6596\/1764\/1\/012049","article-title":"Analysis of contrast limited adaptive histogram equalization (CLAHE) parameters on finger knuckle print identification","volume":"1764","author":"Hana","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"012002","DOI":"10.1088\/1742-6596\/1743\/1\/012002","article-title":"Comparative study of optimization techniques in deep learning: Application in the ophthalmology field","volume":"1743","author":"Mustapha","year":"2021","journal-title":"J. Phys. Conf. Ser."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/3\/221\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:53:04Z","timestamp":1760028784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/3\/221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,13]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["info16030221"],"URL":"https:\/\/doi.org\/10.3390\/info16030221","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,13]]}}}