{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T13:37:30Z","timestamp":1786628250045,"version":"build-2736575974"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":100,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal Academy of Higher Education, Manipal"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Breast cancer is a leading cause of mortality among women globally, highlighting the need for accurate and robust diagnostic systems. This study presents a breast cancer classification based on transformer network (BrCTransNet), a novel deep learning-based framework for breast cancer classification from histopathological images. Initially, input images are preprocessed using an Adaptive Gaussian Bilateral (AGB) filter to reduce noise while preserving essential features. Feature extraction is performed using a Sequential Residual Recurrent Multiscale Attention Network (S2RMANet), which captures rich multi-scale spatial and long-range contextual features. The extracted features are then classified using Optimized Polarized Self-Attention-based Transformer (OPATransNet), a Transformer-based model that integrates a Polarized Self-Attention Module to independently learn spatial and channel-wise dependencies. To optimize the loss function, a Chebyshev StarFish Optimization (ChStF) algorithm is used for tuning OPATransNet hyperparameter, combining Chaotic Chebyshev Mapping with the StarFish Optimization Algorithm (SFOA) to enhance performance. Experimental results using the Breast Histopathology Images dataset demonstrate that the proposed BrCTransNet achieves a classification accuracy of 98.97%, recall of 98.91%, F1-score of 98.98%, specificity of 98.36%, and MSE of 0.0103 outperforming existing models. These findings confirm the superiority of the proposed model in terms of accuracy, robustness, and convergence speed, establishing its potential as a reliable tool for breast cancer diagnosis.<\/jats:p>","DOI":"10.1038\/s41598-026-51422-0","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:45:08Z","timestamp":1777959908000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transformer based breast cancer classification from histopathological images using sequential residual recurrent multiscale attention network"],"prefix":"10.1038","volume":"16","author":[{"given":"V.","family":"Rajeswari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Kathirvel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhinandan","family":"Routray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"issue":"12","key":"51422_CR1","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s10462-024-10984-z","volume":"57","author":"FZ Nakach","year":"2024","unstructured":"Nakach, F. Z., Idri, A. & Goceri, E. A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification. Artif. Intell. Rev. 57(12), 327. https:\/\/doi.org\/10.1007\/s10462-024-10984-z (2024).","journal-title":"Artif. Intell. Rev."},{"issue":"1","key":"51422_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-57740-5","volume":"14","author":"T Islam","year":"2024","unstructured":"Islam, T. et al. Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI. Sci. Rep. 14(1), 8487. https:\/\/doi.org\/10.1038\/s41598-024-57740-5 (2024).","journal-title":"Sci. Rep."},{"issue":"1","key":"51422_CR3","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1186\/s13058-024-01895-6","volume":"26","author":"B Jiang","year":"2024","unstructured":"Jiang, B. et al. Deep learning applications in breast cancer histopathological imaging: Diagnosis, treatment, and prognosis. Breast Cancer Res. 26(1), 137. https:\/\/doi.org\/10.1186\/s13058-024-01895-6 (2024).","journal-title":"Breast Cancer Res."},{"issue":"7","key":"51422_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0304757","volume":"19","author":"J Ahmad","year":"2024","unstructured":"Ahmad, J. et al. Deep learning empowered breast cancer diagnosis: Advancements in detection and classification. PLoS ONE 19(7), e0304757. https:\/\/doi.org\/10.1371\/journal.pone.0304757 (2024).","journal-title":"PLoS ONE"},{"issue":"4","key":"51422_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrras.2024.101080","volume":"17","author":"S \u00dcnalan","year":"2024","unstructured":"\u00dcnalan, S., G\u00fcnay, O., Akkurt, I., Gunoglu, K. & Tekin, H. O. A comparative study on breast cancer classification with stratified shuffle split and K-fold cross validation via ensembled machine learning. J. Radiat. Res. Appl. Sci. 17(4), 101080. https:\/\/doi.org\/10.1016\/j.jrras.2024.101080 (2024).","journal-title":"J. Radiat. Res. Appl. Sci."},{"issue":"1","key":"51422_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3702237","volume":"6","author":"M Di Giammarco","year":"2025","unstructured":"Di Giammarco, M. et al. Explainable deep learning for breast cancer classification and localization. ACM Trans. Comput. Healthc. 6(1), 1\u201318. https:\/\/doi.org\/10.1145\/3702237 (2025).","journal-title":"ACM Trans. Comput. Healthc."},{"issue":"2","key":"51422_CR7","doi-asserted-by":"publisher","first-page":"149","DOI":"10.2174\/0115748936333380240816053223","volume":"20","author":"W Zhenfei","year":"2025","unstructured":"Zhenfei, W. et al. Hybrid feature extraction for breast cancer classification using the ensemble residual VGG16 deep learning model. Curr. Bioinform. 20(2), 149\u2013163. https:\/\/doi.org\/10.2174\/0115748936333380240816053223 (2025).","journal-title":"Curr. Bioinform."},{"issue":"1","key":"51422_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-87826-7","volume":"15","author":"E Sreehari","year":"2025","unstructured":"Sreehari, E. & Dhinesh Babu, L. D. A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning. Sci. Rep. 15(1), 4171. https:\/\/doi.org\/10.1038\/s41598-025-87826-7 (2025).","journal-title":"Sci. Rep."},{"key":"51422_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-025-10984-2","author":"I Jahan","year":"2025","unstructured":"Jahan, I. et al. Deep learning and vision transformers-based framework for breast cancer and subtype identification. Neural Comput. Appl. https:\/\/doi.org\/10.1007\/s00521-025-10984-2 (2025).","journal-title":"Neural Comput. Appl."},{"key":"51422_CR10","doi-asserted-by":"publisher","DOI":"10.1177\/20552076241313161","volume":"11","author":"A Ponraj","year":"2025","unstructured":"Ponraj, A. et al. A multi-patch-based deep learning model with VGG19 for breast cancer classifications in the pathology images. Digit. Health 11, 20552076241313161. https:\/\/doi.org\/10.1177\/20552076241313161 (2025).","journal-title":"Digit. Health"},{"issue":"12","key":"51422_CR11","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.3390\/cancers16122222","volume":"16","author":"AA Balasubramanian","year":"2024","unstructured":"Balasubramanian, A. A. et al. Ensemble deep learning-based image classification for breast cancer subtype and invasiveness diagnosis from whole slide image histopathology. Cancers 16(12), 2222. https:\/\/doi.org\/10.3390\/cancers16122222 (2024).","journal-title":"Cancers"},{"key":"51422_CR12","doi-asserted-by":"publisher","first-page":"12869","DOI":"10.1109\/ACCESS.2024.3356602","volume":"12","author":"A Batool","year":"2024","unstructured":"Batool, A. & Byun, Y. C. Toward improving breast cancer classification using an adaptive voting ensemble learning algorithm. IEEE Access 12, 12869\u201312882. https:\/\/doi.org\/10.1109\/ACCESS.2024.3356602 (2024).","journal-title":"IEEE Access"},{"key":"51422_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibmed.2025.100222","author":"R Qasrawi","year":"2025","unstructured":"Qasrawi, R. et al. Advancing breast cancer detection in ultrasound images using a novel hybrid ensemble deep learning model. Intell.-Based Med. https:\/\/doi.org\/10.1016\/j.ibmed.2025.100222 (2025).","journal-title":"Intell.-Based Med."},{"key":"51422_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2025.104559","author":"AR Yadav","year":"2025","unstructured":"Yadav, A. R. & Kumar, V. N. PSO-optimized fractional order CNNs for enhanced breast cancer detection. Results Eng. https:\/\/doi.org\/10.1016\/j.rineng.2025.104559 (2025).","journal-title":"Results Eng."},{"key":"51422_CR15","doi-asserted-by":"crossref","unstructured":"Wang, W., Li, Y., Yan, X., Xiao, M. & Gao, M. Breast cancer image classification method based on deep transfer learning. In\u00a0Proceedings of the International Conference on Image Processing, Machine Learning and Pattern Recognition\u00a0190\u2013197 (2024).","DOI":"10.1145\/3700906.3700937"},{"issue":"1","key":"51422_CR16","doi-asserted-by":"publisher","first-page":"12082","DOI":"10.1038\/s41598-025-95311-4","volume":"15","author":"UK Lilhore","year":"2025","unstructured":"Lilhore, U. K. et al. Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification. Sci. Rep. 15(1), 12082. https:\/\/doi.org\/10.1038\/s41598-025-95311-4 (2025).","journal-title":"Sci. Rep."},{"key":"51422_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibmed.2024.100193","volume":"11","author":"A La Moglia","year":"2025","unstructured":"La Moglia, A. & Almustafa, K. M. Breast cancer prediction using machine learning classification algorithms. Intell.-Based Med. 11, 100193. https:\/\/doi.org\/10.1016\/j.ibmed.2024.100193 (2025).","journal-title":"Intell.-Based Med."},{"issue":"1","key":"51422_CR18","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41698-024-00772-x","volume":"9","author":"A Keogan","year":"2025","unstructured":"Keogan, A. et al. Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging. NPJ Precision Oncol. 9(1), 18. https:\/\/doi.org\/10.1038\/s41698-024-00772-x (2025).","journal-title":"NPJ Precision Oncol."},{"issue":"3","key":"51422_CR19","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1016\/j.ultrasmedbio.2024.11.020","volume":"51","author":"Y Cho","year":"2025","unstructured":"Cho, Y. et al. Attention-based fusion network for breast cancer segmentation and classification using multi-modal ultrasound images. Ultrasound Med. Biol. 51(3), 568\u2013577. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2024.11.020 (2025).","journal-title":"Ultrasound Med. Biol."},{"key":"51422_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108553","volume":"260","author":"P Bedi","year":"2025","unstructured":"Bedi, P., Rani, S., Gupta, B., Bhasin, V. & Gole, P. EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data. Comput. Methods Programs Biomed. 260, 108553. https:\/\/doi.org\/10.1016\/j.cmpb.2024.108553 (2025).","journal-title":"Comput. Methods Programs Biomed."},{"issue":"1","key":"51422_CR21","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s44147-025-00589-w","volume":"72","author":"M Yusuf","year":"2025","unstructured":"Yusuf, M., Kana, A. F. D., Bagiwa, M. A. & Abdullahi, M. Multi-classification of breast cancer histopathological image using enhanced shallow convolutional neural network. J. Eng. Appl. Sci. 72(1), 24. https:\/\/doi.org\/10.1186\/s44147-025-00589-w (2025).","journal-title":"J. Eng. Appl. Sci."},{"issue":"1","key":"51422_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/s42492-024-00181-8","volume":"8","author":"ML Abimouloud","year":"2025","unstructured":"Abimouloud, M. L., Bensid, K., Elleuch, M., Ammar, M. B. & Kherallah, M. Advancing breast cancer diagnosis: Token vision transformers for faster and accurate classification of histopathology images. Vis. Comput. Ind. Biomed. Art. 8(1), 1. https:\/\/doi.org\/10.1186\/s42492-024-00181-8 (2025).","journal-title":"Vis. Comput. Ind. Biomed. Art."},{"key":"51422_CR23","unstructured":"Hao, J., Liu, Y., Zeng, S. & He, Y. FECT: Classification of Breast Cancer Pathological Images Based on Fusion Features.\u00a0arXiv preprint arXiv:2501.10128. (2025)."},{"issue":"1","key":"51422_CR24","doi-asserted-by":"publisher","first-page":"240","DOI":"10.3390\/s25010240","volume":"25","author":"J Li","year":"2025","unstructured":"Li, J., Wang, K. & Jiang, X. Robust multi-subtype identification of breast cancer pathological images based on a dual-branch frequency domain fusion network. Sensors (Basel) 25(1), 240. https:\/\/doi.org\/10.3390\/s25010240 (2025).","journal-title":"Sensors (Basel)"},{"key":"51422_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2025.3536424","author":"S Majumder","year":"2025","unstructured":"Majumder, S., Paul, A., Schwenker, F. & Sarkar, R. SAWL-Net: A statistical attentions and wavelet aided lightweight network for classification of cancers in histopathological images. IEEE Trans. Artif. Intell. https:\/\/doi.org\/10.1109\/TAI.2025.3536424 (2025).","journal-title":"IEEE Trans. Artif. Intell."},{"key":"51422_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3615654","author":"K Guzel","year":"2025","unstructured":"Guzel, K. & Bilgin, G. HFT-Net: Hybrid fusion transformer network for multi-source breast cancer classification. IEEE Access https:\/\/doi.org\/10.1109\/ACCESS.2025.3615654 (2025).","journal-title":"IEEE Access"},{"key":"51422_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108939","volume":"136","author":"NA Wani","year":"2024","unstructured":"Wani, N. A., Kumar, R. & Bedi, J. Harnessing fusion modeling for enhanced breast cancer classification through interpretable artificial intelligence and in-depth explanations. Eng. Appl. Artif. Intell. 136, 108939. https:\/\/doi.org\/10.1016\/j.engappai.2024.108939 (2024).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"51422_CR28","unstructured":"Breast Histopathology Images, \u201chttps:\/\/www.kaggle.com\/datasets\/paultimothymooney\/breast-histopathology-images\u201d Accessed on Jan 2025"},{"issue":"1","key":"51422_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-024-00296-5","volume":"12","author":"S Alazwari","year":"2024","unstructured":"Alazwari, S. et al. Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model. Health Inf. Sci. Syst. 12(1), 38. https:\/\/doi.org\/10.1007\/s13755-024-00296-5 (2024).","journal-title":"Health Inf. Sci. Syst."},{"key":"51422_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119419","volume":"216","author":"TR Mim","year":"2023","unstructured":"Mim, T. R. et al. GRU-INC: An inception-attention based approach using GRU for human activity recognition. Expert Syst. Appl. 216, 119419. https:\/\/doi.org\/10.1016\/j.eswa.2022.119419 (2023).","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"51422_CR31","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.1007\/s00521-024-10694-1","volume":"37","author":"C Zhong","year":"2025","unstructured":"Zhong, C. et al. Starfish optimization algorithm (SFOA): A bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers. Neural Comput. Appl. 37(5), 3641\u20133683. https:\/\/doi.org\/10.1007\/s00521-024-10694-1 (2025).","journal-title":"Neural Comput. Appl."},{"key":"51422_CR32","doi-asserted-by":"publisher","first-page":"184119","DOI":"10.1109\/ACCESS.2024.3503413","volume":"12","author":"M Hayat","year":"2024","unstructured":"Hayat, M., Ahmad, N., Nasir, A. & Tariq, Z. A. Hybrid deep learning efficientnetv2 and vision transformer (effnetv2-vit) model for breast cancer histopathological image classification. IEEE Access 12, 184119\u2013184131. https:\/\/doi.org\/10.1109\/ACCESS.2024.3503413 (2024).","journal-title":"IEEE Access"},{"issue":"10","key":"51422_CR33","doi-asserted-by":"publisher","first-page":"5025","DOI":"10.1109\/JBHI.2022.3187765","volume":"26","author":"M Liu","year":"2022","unstructured":"Liu, M. et al. A deep learning method for breast cancer classification in the pathology images. IEEE J. Biomed. Health Inform. 26(10), 5025\u20135032. https:\/\/doi.org\/10.1109\/JBHI.2022.3187765 (2022).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"51422_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104534","volume":"81","author":"F Atban","year":"2023","unstructured":"Atban, F., Ekinci, E. & Garip, Z. Traditional machine learning algorithms for breast cancer image classification with optimized deep features. Biomed. Signal Process. Control. 81, 104534. https:\/\/doi.org\/10.1016\/j.bspc.2022.104534 (2023).","journal-title":"Biomed. Signal Process. Control."},{"issue":"1","key":"51422_CR35","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/6530719","volume":"2023","author":"H Chen","year":"2023","unstructured":"Chen, H. et al. Classification prediction of breast cancer based on machine learning. Comput. Intell. Neurosci. 2023(1), 6530719. https:\/\/doi.org\/10.1155\/2023\/6530719 (2023).","journal-title":"Comput. Intell. Neurosci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-51422-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-51422-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-51422-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T13:03:13Z","timestamp":1786626193000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-51422-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,5]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["51422"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-51422-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,5]]},"assertion":[{"value":"18 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. No experiment was performed on the human body and living organism\/ animal. So, ethical approval from an ethical committee is not required.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":4,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No.","order":5,"name":"Ethics","label":"Dual publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No.","order":6,"name":"Ethics","label":"Third party material","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":7,"name":"Ethics","label":"Clinical trial no.","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"25277"}}