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This study aimed to conduct a comprehensive comparison of three systems a self-attention transformer (VIT), a compact convolution transformer (CCT), and a tokenlearner (TVIT) for binary classification of mammography images into benign and cancerous tissue. Thorough experiments were performed using the DDSM dataset, which consists of 5970 benign and 7158 malignant images. The performance accuracy of the proposed models was evaluated, yielding results of 99.81% for VIT, 99.92% for CCT, and 99.05% for TVIT. Additionally, the study compared these results with the current state-of-the-art performance metrics. The findings demonstrate how convolution-attention mechanisms can effectively contribute to the development of robust computer-aided systems for diagnosing breast cancer. Notably, the proposed approach achieves high-performance results while also minimizing the computational resources required and reducing decision time.<\/jats:p>","DOI":"10.3233\/his-240002","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T12:14:01Z","timestamp":1716552841000},"page":"67-83","source":"Crossref","is-referenced-by-count":6,"title":["Vision transformer-convolution for breast cancer classification using mammography images: A comparative study"],"prefix":"10.1177","volume":"20","author":[{"given":"Mouhamed Laid","family":"Abimouloud","sequence":"first","affiliation":[{"name":"National Engineering School of Sfax, University of Sfax, Sfax, Tunisia"},{"name":"Advanced Technologies for Environment and Smart Cities (ATES Unit), University of Sfax, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khaled","family":"Bensid","sequence":"additional","affiliation":[{"name":"Laboratory of Electrical Engineering (LAGE), University of KASDI Merbah Ouargla, Ouargla, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Elleuch","sequence":"additional","affiliation":[{"name":"National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia"},{"name":"Advanced Technologies for Environment and Smart Cities (ATES Unit), University of Sfax, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oussama","family":"Aiadi","sequence":"additional","affiliation":[{"name":"Artifcial Intelligence and Information Technology Laboratory (LINATI), Kasdi Merbah University, Ouargla, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monji","family":"Kherallah","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, University of Sfax, Sfax, Tunisia"},{"name":"Advanced Technologies for Environment and Smart Cities (ATES Unit), University of Sfax, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/HIS-240002_ref1","unstructured":"R. Agarwal et al., Computer aided detection for breast lesion in ultrasound and mammography (2019)."},{"key":"10.3233\/HIS-240002_ref2","unstructured":"R. Maalej, A. Mezghani, M. Elleuch, M. Kherallah et al., Transfer learning and data augmentation for improved breast cancer histopathological images classifier, International Journal of Computer Information Systems & Industrial Management Applications 15, (2023)."},{"key":"10.3233\/HIS-240002_ref3","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3233\/HIS-220009","article-title":"Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images","volume":"18","author":"Appari","year":"2022","journal-title":"Int. J. Hybrid Intell. Syst"},{"key":"10.3233\/HIS-240002_ref4","doi-asserted-by":"crossref","unstructured":"Z. Rustam, V. Hapsari and M. Solihin, Optimal cervical cancer classification using gauss-newton representation based algorithm, Vol. 2168 (AIP Publishing, 2019).","DOI":"10.1063\/1.5132472"},{"key":"10.3233\/HIS-240002_ref5","unstructured":"S. Bharati, P. Podder and M. Mondal, Artificial neural network based breast cancer screening: a comprehensive review, International Journal of Computer Information Systems & Industrial Management Applications 12 (2020)."},{"key":"10.3233\/HIS-240002_ref6","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.3390\/math11061429","article-title":"Multi-method diagnosis of histopathological images for early detection of breast cancer based on hybrid and deep learning","volume":"11","author":"Al-Jabbar","year":"2023","journal-title":"Mathematics"},{"key":"10.3233\/HIS-240002_ref7","doi-asserted-by":"crossref","unstructured":"K. Das, S. Conjeti, A.G. Roy, J. Chatterjee and D. Sheet, Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification, IEEE, 2018, pp.\u00a0578\u2013581.","DOI":"10.1109\/ISBI.2018.8363642"},{"key":"10.3233\/HIS-240002_ref8","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1007\/s12553-021-00592-0","article-title":"Classification of breast cancer types, sub-types and grade from histopathological images using deep learning technique","volume":"11","author":"Zewdie","year":"2021","journal-title":"Health and Technology"},{"key":"10.3233\/HIS-240002_ref10","doi-asserted-by":"crossref","first-page":"e0262349","DOI":"10.1371\/journal.pone.0262349","article-title":"Deep learning model for fully automated breast cancer detection system from thermograms","volume":"17","author":"Mohamed","year":"2022","journal-title":"PloS One"},{"key":"10.3233\/HIS-240002_ref11","doi-asserted-by":"crossref","first-page":"e0276523","DOI":"10.1371\/journal.pone.0276523","article-title":"A novel cnn pooling layer for breast cancer segmentation and classification from thermograms","volume":"17","author":"Mohamed","year":"2022","journal-title":"Plos One"},{"key":"10.3233\/HIS-240002_ref12","unstructured":"E.U. Henry, O. Emebob and C.A. Omonhinmin, Vision transformers in medical imaging: A review. arXiv preprint arXiv:221110043. (2022)."},{"key":"10.3233\/HIS-240002_ref13","doi-asserted-by":"crossref","unstructured":"X. Zhu, D. Cheng, Z. Zhang, S. Lin and J. Dai, An empirical study of spatial attention mechanisms in deep networks, 2019, pp.\u00a06688\u20136697.","DOI":"10.1109\/ICCV.2019.00679"},{"key":"10.3233\/HIS-240002_ref14","unstructured":"A. Dosovitskiy et al., An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929. (2020)."},{"key":"10.3233\/HIS-240002_ref15","unstructured":"S. Mehta and M. Rastegari, Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:211002178. (2021)."},{"key":"10.3233\/HIS-240002_ref16","doi-asserted-by":"publisher","first-page":"163","DOI":"10.3233\/HIS-220002","article-title":"Crossvit wide residual squeeze-and-excitation network for alzheimer\u2019s disease classification with self attention progan data augmentation","volume":"17","author":"Kadri","year":"2021","journal-title":"Int. J. Hybrid Intell. Syst."},{"key":"10.3233\/HIS-240002_ref17","unstructured":"A. Benlamoudi et al., Deep Neural Networks Improve Radiologists\u2019 Performance in Breast Cancer Screening. Ph.D. thesis, UNIVERSITY OF OUARGLA."},{"key":"10.3233\/HIS-240002_ref18","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.imed.2022.07.002","article-title":"Transformers in medical image analysis","volume":"3","author":"He","year":"2023","journal-title":"Intelligent Medicine"},{"key":"10.3233\/HIS-240002_ref19","unstructured":"M.L. Abimouloud, K. Bensid, M. Elleuch, O. Aiadi and M. Kherallah, Mammography breast cancer classification using vision transformers. 23th International Conference on Intelligent Systems Design and Applications (ISDA 2023) (December 11\u201313, 2023)."},{"key":"10.3233\/HIS-240002_ref20","first-page":"1","article-title":"New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques","author":"Boudouh","year":"2023","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/HIS-240002_ref21","doi-asserted-by":"crossref","first-page":"178","DOI":"10.3390\/diagnostics13020178","article-title":"Vision-transformer-based transfer learning for mammogram classification","volume":"13","author":"Ayana","year":"2023","journal-title":"Diagnostics"},{"key":"10.3233\/HIS-240002_ref22","doi-asserted-by":"crossref","first-page":"4701","DOI":"10.1016\/j.aej.2021.03.048","article-title":"Deep learning in mammography images segmentation and classification: Automated cnn approach","volume":"60","author":"Salama","year":"2021","journal-title":"Alexandria Engineering Journal"},{"key":"10.3233\/HIS-240002_ref23","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1007\/s00521-023-09165-w","article-title":"Breast lesion classification from mammograms using deep neural network and test-time augmentation","volume":"36","author":"Oza","year":"2024","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/HIS-240002_ref24","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1049\/ipr2.12035","article-title":"Classification of breast mass in two-view mammograms via deep learning","volume":"15","author":"Li","year":"2021","journal-title":"IET Image Processing"},{"key":"10.3233\/HIS-240002_ref25","doi-asserted-by":"crossref","first-page":"105928","DOI":"10.1016\/j.dib.2020.105928","article-title":"Dataset of breast mammography images with masses","volume":"31","author":"Huang","year":"2020","journal-title":"Data in Brief"},{"key":"10.3233\/HIS-240002_ref26","unstructured":"A. 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Angelova, Tokenlearner: Adaptive space-time tokenization for videos, Advances in Neural Information Processing Systems 34 (2021), 12786\u201312797."}],"container-title":["International Journal of Hybrid Intelligent Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/HIS-240002","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:53:02Z","timestamp":1777452782000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/HIS-240002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,11]]},"references-count":26,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/his-240002","relation":{},"ISSN":["1448-5869","1875-8819"],"issn-type":[{"value":"1448-5869","type":"print"},{"value":"1875-8819","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,11]]}}}