{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T16:30:24Z","timestamp":1793896224425,"version":"4.8.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-25399-1","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:51:39Z","timestamp":1762516299000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Task optimized vision transformer for diabetic retinopathy detection and classification in resource constrained early diagnosis settings"],"prefix":"10.1038","volume":"15","author":[{"given":"Ramasubramanian","family":"Bhoopalan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priyadharshini","family":"Sekar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Nagaprasad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tadesse Regassa","family":"Mamo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramaswamy","family":"Krishnaraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"25399_CR1","doi-asserted-by":"publisher","first-page":"73950","DOI":"10.1109\/ACCESS.2024.3405570","volume":"12","author":"K Ahnaf Alavee","year":"2024","unstructured":"Ahnaf Alavee, K. et al. Enhancing early detection of diabetic retinopathy through the integration of deep learning models and explainable artificial intelligence. IEEE Access 12, 73950\u201373969. https:\/\/doi.org\/10.1109\/ACCESS.2024.3405570 (2024).","journal-title":"IEEE Access"},{"key":"25399_CR2","doi-asserted-by":"publisher","first-page":"113565","DOI":"10.1109\/ACCESS.2024.3432867","volume":"12","author":"K Nazir","year":"2024","unstructured":"Nazir, K., Kim, J. & Byun, Y.-C. Enhancing early-stage diabetic retinopathy detection using a weighted ensemble of deep neural networks. IEEE Access 12, 113565\u2013113579. https:\/\/doi.org\/10.1109\/ACCESS.2024.3432867 (2024).","journal-title":"IEEE Access"},{"key":"25399_CR3","doi-asserted-by":"publisher","first-page":"61408","DOI":"10.1109\/ACCESS.2021.3074422","volume":"9","author":"Z Khan","year":"2021","unstructured":"Khan, Z. et al. Diabetic retinopathy detection using VGG-NIN: A deep learning architecture. IEEE Access 9, 61408\u201361416. https:\/\/doi.org\/10.1109\/ACCESS.2021.3074422 (2021).","journal-title":"IEEE Access"},{"key":"25399_CR4","doi-asserted-by":"publisher","first-page":"42361","DOI":"10.1109\/ACCESS.2023.3272228","volume":"11","author":"MAK Raiaan","year":"2023","unstructured":"Raiaan, M. A. K. et al. A lightweight robust deep learning model gained high accuracy in classifying a wide range of diabetic retinopathy images. IEEE Access 11, 42361\u201342388. https:\/\/doi.org\/10.1109\/ACCESS.2023.3272228 (2023).","journal-title":"IEEE Access"},{"key":"25399_CR5","doi-asserted-by":"publisher","unstructured":"Al-Tayeb, K. A., Jarndal, A., & Bonny, T. Vision transformer for diabetic retinopathy diagnosis: A deep learning approach beyond CNN. In 2025 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), Moscow, Russian Federation, pp. 1\u20135, https:\/\/doi.org\/10.1109\/REEPE63962.2025.10970953 (2025).","DOI":"10.1109\/REEPE63962.2025.10970953"},{"key":"25399_CR6","doi-asserted-by":"publisher","unstructured":"Mohan, N. J., Murugan, R., Goel, T., & Roy, P. ViT-DR: vision transformers in diabetic retinopathy grading using fundus images. In 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, pp. 167\u2013172, https:\/\/doi.org\/10.1109\/R10-HTC54060.2022.9930027 (2022).","DOI":"10.1109\/R10-HTC54060.2022.9930027"},{"key":"25399_CR7","doi-asserted-by":"publisher","first-page":"117546","DOI":"10.1109\/ACCESS.2023.3326528","volume":"11","author":"W Nazih","year":"2023","unstructured":"Nazih, W., Aseeri, A. O., Atallah, O. Y. & El-Sappagh, S. Vision transformer model for predicting the severity of diabetic retinopathy in fundus photography-based retina images. IEEE Access 11, 117546\u2013117561. https:\/\/doi.org\/10.1109\/ACCESS.2023.3326528 (2023).","journal-title":"IEEE Access"},{"key":"25399_CR8","doi-asserted-by":"publisher","first-page":"41344","DOI":"10.1109\/ACCESS.2021.3065273","volume":"9","author":"F Saeed","year":"2021","unstructured":"Saeed, F., Hussain, M. & Aboalsamh, H. A. Automatic diabetic retinopathy diagnosis using adaptive fine-tuned convolutional neural network. IEEE Access 9, 41344\u201341359. https:\/\/doi.org\/10.1109\/ACCESS.2021.3065273 (2021).","journal-title":"IEEE Access"},{"key":"25399_CR9","doi-asserted-by":"publisher","unstructured":"Saranya, K., Ramasubramanian, B. & Kaja Mohideen, S. A novel approach for the detection of new vessels in the retinal images for screening diabetic retinopathy. In Proceedings of the 2012 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, IEEE, pp. 57\u201361. https:\/\/doi.org\/10.1109\/ICCSP.2012.6208394 (2012).","DOI":"10.1109\/ICCSP.2012.6208394"},{"key":"25399_CR10","doi-asserted-by":"publisher","first-page":"120554","DOI":"10.1109\/ACCESS.2023.3327900","volume":"11","author":"H Naz","year":"2023","unstructured":"Naz, H. et al. Ensembled deep convolutional generative adversarial network for grading imbalanced diabetic retinopathy recognition. IEEE Access 11, 120554\u2013120568. https:\/\/doi.org\/10.1109\/ACCESS.2023.3327900 (2023).","journal-title":"IEEE Access"},{"key":"25399_CR11","doi-asserted-by":"publisher","first-page":"54190","DOI":"10.1109\/ACCESS.2021.3070685","volume":"9","author":"MT Al-Antary","year":"2021","unstructured":"Al-Antary, M. T. & Arafa, Y. Multi-scale attention network for diabetic retinopathy classification. IEEE Access 9, 54190\u201354200. https:\/\/doi.org\/10.1109\/ACCESS.2021.3070685 (2021).","journal-title":"IEEE Access"},{"key":"25399_CR12","doi-asserted-by":"publisher","first-page":"34387","DOI":"10.1109\/ACCESS.2020.2974158","volume":"8","author":"M Ghazal","year":"2020","unstructured":"Ghazal, M., Ali, S. S., Mahmoud, A. H., Shalaby, A. M. & El-Baz, A. Accurate detection of non-proliferative diabetic retinopathy in optical coherence tomography images using convolutional neural networks. IEEE Access 8, 34387\u201334397. https:\/\/doi.org\/10.1109\/ACCESS.2020.2974158 (2020).","journal-title":"IEEE Access"},{"key":"25399_CR13","doi-asserted-by":"publisher","first-page":"151133","DOI":"10.1109\/ACCESS.2020.3015258","volume":"8","author":"R Sarki","year":"2020","unstructured":"Sarki, R., Ahmed, K., Wang, H. & Zhang, Y. Automatic detection of diabetic eye disease through deep learning using fundus images: A survey. IEEE Access 8, 151133\u2013151149. https:\/\/doi.org\/10.1109\/ACCESS.2020.3015258 (2020).","journal-title":"IEEE Access"},{"issue":"4","key":"25399_CR14","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1109\/TCSS.2022.3213369","volume":"10","author":"A Kukkar","year":"2023","unstructured":"Kukkar, A. et al. Optimizing deep learning model parameters using socially implemented IoMT systems for diabetic retinopathy classification problem. IEEE Trans. Comput. Soc. Syst. 10(4), 1654\u20131665. https:\/\/doi.org\/10.1109\/TCSS.2022.3213369 (2023).","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"5","key":"25399_CR15","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TMI.2019.2951844","volume":"39","author":"X Li","year":"2020","unstructured":"Li, X. et al. CANet: Cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans. Med. Imaging 39(5), 1483\u20131493 (2020).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"25399_CR16","doi-asserted-by":"publisher","first-page":"5503905","DOI":"10.1109\/LGRS.2023.3255867","volume":"20","author":"H Zhou","year":"2023","unstructured":"Zhou, H., Zhang, X., Zhang, C. & Ma, Q. Vision transformer with contrastive learning for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 20, 5503905. https:\/\/doi.org\/10.1109\/LGRS.2023.3255867 (2023).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"25399_CR17","doi-asserted-by":"publisher","first-page":"28642","DOI":"10.1109\/ACCESS.2022.3157632","volume":"10","author":"MZ Atwany","year":"2022","unstructured":"Atwany, M. Z., Sahyoun, A. H. & Yaqub, M. Deep learning techniques for diabetic retinopathy classification: A survey. IEEE Access 10, 28642\u201328655. https:\/\/doi.org\/10.1109\/ACCESS.2022.3157632 (2022).","journal-title":"IEEE Access"},{"key":"25399_CR18","doi-asserted-by":"publisher","unstructured":"Bhoopalan, R., Anbalagan, S., Mathu, V., & Sabpathy, S. An efficient AI based system for the detection of diabetic maculopathy using colour fundus images. In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), Karaikal, India, 2024, pp. 1\u20138, https:\/\/doi.org\/10.1109\/IConSCEPT61884.2024.10627839.","DOI":"10.1109\/IConSCEPT61884.2024.10627839"},{"key":"25399_CR19","doi-asserted-by":"publisher","first-page":"27973","DOI":"10.1038\/s41598-025-13155-4","volume":"15","author":"S Priyadharshini","year":"2025","unstructured":"Priyadharshini, S. et al. A successive framework for brain tumor interpretation using Yolo variants. Sci. Rep. 15, 27973. https:\/\/doi.org\/10.1038\/s41598-025-13155-4 (2025).","journal-title":"Sci. Rep."},{"key":"25399_CR20","doi-asserted-by":"publisher","first-page":"137881","DOI":"10.1109\/ACCESS.2023.3339574","volume":"11","author":"M Vadduri","year":"2023","unstructured":"Vadduri, M. & Kuppusamy, P. Enhancing ocular healthcare: deep learning-based multi-class diabetic eye disease segmentation and classification. IEEE Access 11, 137881\u2013137898. https:\/\/doi.org\/10.1109\/ACCESS.2023.3339574 (2023).","journal-title":"IEEE Access"},{"key":"25399_CR21","doi-asserted-by":"publisher","unstructured":"Dhaoui, M. & Zrelli, A. Retinal diseases classification system using OCT images combined with CNN models. In Proceedings of the 2023 International Symposium on Networks, Computers and Communications (ISNCC), Doha, Qatar, pp. 1\u20136. Available at: https:\/\/doi.org\/10.1109\/ISNCC58260.2023.10323745 (2023).","DOI":"10.1109\/ISNCC58260.2023.10323745"},{"key":"25399_CR22","doi-asserted-by":"publisher","unstructured":"Sarnaik, N., Gautam, A., Kushwaha, S., & Shanker, R. Convolutional vision transformer based automatic grading of diabetic retinopathy images. In 2024 IEEE 8th International Conference on Information and Communication Technology (CICT), Prayagraj UP, India, pp. 1\u20135, https:\/\/doi.org\/10.1109\/CICT64037.2024.10899535 (2024).","DOI":"10.1109\/CICT64037.2024.10899535"},{"key":"25399_CR23","doi-asserted-by":"publisher","unstructured":"Kakade, G., & Kakade, C. Optimal detection of diabetic retinopathy severity levels using attention-based CNN and vision transformers (ViT). In 2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom), Dubai, United Arab Emirates, pp. 249\u2013253, https:\/\/doi.org\/10.1109\/MoSICom63082.2024.10881457 (2024).","DOI":"10.1109\/MoSICom63082.2024.10881457"},{"key":"25399_CR24","doi-asserted-by":"publisher","unstructured":"Dai, W., Mou, C., Wu, J., & Ye, X. Diabetic retinopathy detection with enhanced vision transformers: the twins-PCPVT solution. In 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, , pp. 403\u2013407, https:\/\/doi.org\/10.1109\/ICETCI57876.2023.10176810 (2023).","DOI":"10.1109\/ICETCI57876.2023.10176810"},{"key":"25399_CR25","doi-asserted-by":"publisher","unstructured":"Sivakumar, S., Kumar, P. L., Reddy, G. N., Anandaraj, B., Kumar, T. R., & Palanisamy, P. Vision transformer approaches for diabetic retinopathy. In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), Faridabad, India, pp. 886\u2013891, https:\/\/doi.org\/10.1109\/ICAICCIT64383.2024.10912186 (2024).","DOI":"10.1109\/ICAICCIT64383.2024.10912186"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-25399-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-25399-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-25399-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:51:39Z","timestamp":1762516299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-25399-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["25399"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-25399-1","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]},"assertion":[{"value":"29 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39047"}}