{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:14:53Z","timestamp":1785428093326,"version":"3.56.0"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea government","award":["RS-2023-00218176"],"award-info":[{"award-number":["RS-2023-00218176"]}]},{"DOI":"10.13039\/501100002560","name":"Soonchunhyang University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002560","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/03qjsrb10","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"crossref","award":["PNURSP2026R760"],"award-info":[{"award-number":["PNURSP2026R760"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"crossref"}]},{"award":["PNURSP2026R760"],"award-info":[{"award-number":["PNURSP2026R760"]}],"id":[{"id":"https:\/\/ror.org\/05b0cyh02","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Breast cancer remains the leading cause of cancer-related deaths among women globally. Early detection through mammography is vital for improving survival rates; however, the large volume of medical images and subtle variations in lesion characteristics pose significant challenges to manual interpretation. Recent automated diagnostic models based on deep learning have shown strong potential for breast cancer classification, but challenges such as overfitting, high computational complexity, limited generalization, and insufficient interpretability remain unresolved. This paper proposes a computationally efficient and context-aware deep learning framework for breast cancer classification using transformer-based multi-scale attention mechanisms and explainable artificial intelligence (XAI). The proposed architecture integrates the Hierarchical Multi-Scale Transformer (HMT) and Edge-Aware Local Transformer (ELT) modules to jointly capture global contextual dependencies and boundary-sensitive local representations from mammographic images. ELT improves feature refinement in high-entropy regions, while HMT models global semantic interactions across multiple feature scales. In addition, an Adaptive Contextual Refinement (ACR) module is introduced to preserve semantically consistent feature representations across spatial resolutions. A Meta-Ensemble Classification (MEC) framework combining weighted SVM and K-Nearest Neighbors (KNN) classifiers is further employed using validation-guided class-adaptive weighting. The proposed framework is evaluated on four benchmark mammography datasets, namely CBIS-DDSM, DDSM, INBreast, and MIAS. The proposed model has demonstrated superior accuracy of over 99% across all breast cancer datasets. The model surpassed transformer-based baselines including Swin-T and ViT while maintaining lower parameter complexity and achieving approximately 7% higher accuracy on the CBIS-DDSM dataset. The proposed framework also demonstrated strong cross-dataset generalization and consistently achieved high precision, recall, and F1-score values across all benchmark datasets. To improve model interpretability, Grad-CAM, SHAP, Occlusion Sensitivity Analysis (OSA), and the proposed TIxAI consistency analysis framework are incorporated to provide preliminary explainability assessment for mammographic classification. The explainability analysis demonstrated spatially consistent saliency behavior across benchmark datasets; however, the current evaluation is based on internal saliency consistency rather than external clinical validation using expert lesion annotations. Overall, the proposed framework provides an effective and computationally efficient approach for automated breast cancer classification while improving model explainability and interpretability.<\/jats:p>","DOI":"10.3390\/bioengineering13060657","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:33:14Z","timestamp":1780479194000},"page":"657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Breast Cancer Detection Using Hierarchical Multi-Scale and Edge-Aware Transformer Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1858-7394","authenticated-orcid":false,"given":"Maria Altaib","family":"Badawi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information, College of Science Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehtisham","family":"Arshad","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9930-6053","authenticated-orcid":false,"given":"Armughan","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9520-3174","authenticated-orcid":false,"given":"Oumaima","family":"Saidani","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taoufik","family":"Saidani","sequence":"additional","affiliation":[{"name":"Center for Scientific Research and Entrepreneurship, Northern Border University, Arar 73213, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zepa","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyoung","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9962109","DOI":"10.1155\/2021\/9962109","article-title":"Breast cancer segmentation methods: Current status and future potentials","volume":"2021","author":"Michael","year":"2021","journal-title":"BioMed Res. Int."},{"key":"ref_2","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2252","DOI":"10.1002\/jmri.28960","article-title":"3D breast cancer segmentation in DCE-MRI using deep learning with weak annotation","volume":"59","author":"Park","year":"2024","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"921","DOI":"10.3390\/biomedinformatics4020052","article-title":"Deep Segmentation Techniques for Breast Cancer Diagnosis","volume":"4","author":"Schutte","year":"2024","journal-title":"BioMedInformatics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s12530-023-09527-8","article-title":"Grouped mask region convolution neural networks for improved breast cancer segmentation in mammography images","volume":"15","author":"Sani","year":"2024","journal-title":"Evol. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13503","DOI":"10.1007\/s11042-023-16021-5","article-title":"Modified anisotropic diffusion and level-set segmentation for breast cancer","volume":"83","author":"Olota","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s11547-024-01826-7","article-title":"Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: A prospective multicenter study","volume":"129","author":"Bartolotta","year":"2024","journal-title":"Radiol. Medica"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3849","DOI":"10.1007\/s11227-023-05605-5","article-title":"Multi-threshold segmentation of breast cancer images based on improved dandelion optimization algorithm","volume":"80","author":"Wang","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s12559-023-10214-8","article-title":"A multi-attention triple decoder deep convolution network for breast cancer segmentation using ultrasound images","volume":"16","author":"Umer","year":"2024","journal-title":"Cogn. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lee, J., Lee, G., Kwak, T.Y., Kim, S.W., Jin, M.S., Kim, C., and Chang, H. (2024). MurSS: A multi-resolution selective segmentation model for breast cancer. Bioengineering, 11.","DOI":"10.3390\/bioengineering11050463"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Leow, L.J.H., Azam, A.B., Tan, H.Q., Nei, W.L., Cao, Q., Huang, L., Xie, Y., and Cai, Y. (2024). A convolutional neural network-based auto-segmentation pipeline for breast cancer imaging. Mathematics, 12.","DOI":"10.3390\/math12040616"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s11263-022-01687-5","article-title":"Distribution-sensitive information retention for accurate binary neural network","volume":"131","author":"Qin","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"11689","DOI":"10.1109\/TPAMI.2023.3272925","article-title":"Diverse sample generation: Pushing the limit of generative data-free quantization","volume":"45","author":"Qin","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","first-page":"56838","article-title":"QuantSR: Accurate low-bit quantization for efficient image super-resolution","volume":"36","author":"Qin","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","unstructured":"Qin, H., Ma, X., Zheng, X., Li, X., Zhang, Y., Liu, S., Luo, J., Liu, X., and Magno, M. (2024). Accurate lora-finetuning quantization of llms via information retention. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"109159","DOI":"10.1016\/j.bspc.2025.109159","article-title":"Explainable skin cancer diagnosis with parallel attention mechanism for segmentation and classification","volume":"113","author":"Thaljaoui","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15420","DOI":"10.1038\/s41598-026-41161-7","article-title":"A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans","volume":"16","author":"Yousafzai","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2338","DOI":"10.3390\/biomedinformatics4040127","article-title":"Early Breast Cancer Detection Based on Deep Learning: An Ensemble Approach Applied to Mammograms","volume":"4","author":"Khourdifi","year":"2024","journal-title":"BioMedInformatics"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ahmad, J., Akram, S., Jaffar, A., Ali, Z., Bhatti, S.M., Ahmad, A., and Rehman, S.U. (2024). Deep learning empowered breast cancer diagnosis: Advancements in detection and classification. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0304757"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5564649","DOI":"10.1155\/2024\/5564649","article-title":"Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach","volume":"2024","author":"Shah","year":"2024","journal-title":"Int. J. Intell. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, P., Ma, R., and Toe, T.T. (2023). Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN. Proceedings of the 2023 3rd International Conference on Electronic Engineering (ICEEM), IEEE.","DOI":"10.1109\/ICEEM58740.2023.10319517"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"26287","DOI":"10.1038\/s41598-024-74305-8","article-title":"Integrative hybrid deep learning for enhanced breast cancer diagnosis: Leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset","volume":"14","author":"Murty","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_23","first-page":"101136","article-title":"Advanced deep learning strategies for breast cancer image analysis","volume":"17","author":"Slimi","year":"2024","journal-title":"J. Radiat. Res. Appl. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20552076241313161","DOI":"10.1177\/20552076241313161","article-title":"A multi-patch-based deep learning model with VGG19 for breast cancer classifications in the pathology images","volume":"11","author":"Ponraj","year":"2025","journal-title":"Digit. Health"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108444","DOI":"10.1016\/j.bspc.2025.108444","article-title":"Enhancing diagnostic precision and mammogram classification in breast cancer detection utilizing an optimized Explicit Feature Interaction Aware Graph Neural Network","volume":"112","author":"Thiyagarajan","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"131089","DOI":"10.1016\/j.eswa.2026.131089","article-title":"Breast cancer stage detection by differentiating benign and malignant tumor using L0H-CWSNN and FZB-IS","volume":"307","author":"Yadav","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kumar Saha, D., Hossain, T., Safran, M., Alfarhood, S., Mridha, M., and Che, D. (2024). Segmentation for mammography classification utilizing deep convolutional neural network. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-024-01510-2"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, H., and Martel, A.L. (2025). Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and Convolutional Neural Network. arXiv.","DOI":"10.1117\/1.JMI.12.S2.S22007"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e110823219688","DOI":"10.2174\/1573405620666230811142718","article-title":"Deep Learning Mammography Classification with a Small Set of Data","volume":"20","author":"Michael","year":"2024","journal-title":"Curr. Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109173","DOI":"10.1016\/j.bspc.2025.109173","article-title":"MammXAI: An XAI integrated adaptive multi-model deep learning approach for breast cancer detection using multi-modality images","volume":"113","author":"Singh","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"24273","DOI":"10.1109\/ACCESS.2021.3056516","article-title":"Breast cancer classification from histopathological images using patch-based deep learning modeling","volume":"9","author":"Hirra","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zeebaree, D.Q., Haron, H., Abdulazeez, A.M., and Zebari, D.A. (2019). Machine learning and region growing for breast cancer segmentation. Proceedings of the 2019 International Conference on Advanced Science and Engineering (ICOASE), IEEE.","DOI":"10.1109\/ICOASE.2019.8723832"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/s13198-022-01696-0","article-title":"Early predictive model for breast cancer classification using blended ensemble learning","volume":"15","author":"Mahesh","year":"2024","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jabeen, K., Khan, M.A., Hameed, M.A., Alqahtani, O., Alouane, M., and Masood, A. (2024). A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1347856"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","unstructured":"Lundberg, S.M., and Lee, S.I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_37","unstructured":"Paszke, A. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv."},{"key":"ref_38","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014, Springer. Proceedings, Part I 13."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K., and Munishkumaran, S. (1998). Current status of the digital database for screening mammography. Digital Mammography: Nijmegen, 1998, Springer.","DOI":"10.1007\/978-94-011-5318-8_75"},{"key":"ref_40","unstructured":"Sawyer-Lee, R., Gimenez, F., Hoogi, A., and Rubin, D. (2016). Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), The Cancer Imaging Archive."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","article-title":"Inbreast: Toward a full-field digital mammographic database","volume":"19","author":"Moreira","year":"2012","journal-title":"Acad. Radiol."},{"key":"ref_42","unstructured":"Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., and Kok, S. (2015). Mammographic Image Analysis Society (Mias) Database v1. 21, University of Cambridge."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108386","DOI":"10.1109\/ACCESS.2023.3311892","article-title":"Breast Cancer detection using deep learning: An Investigation using the DDSM dataset and a customized AlexNet and Support Vector Machine","volume":"11","author":"Ahmad","year":"2023","journal-title":"IEEE Access"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/657\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:39:38Z","timestamp":1780634378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,3]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["bioengineering13060657"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13060657","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,3]]}}}