{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T03:33:16Z","timestamp":1793935996712,"version":"4.8.0"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide, highlighting the critical need for accurate histopathological diagnosis and reliable decision-support systems to improve diagnostic sensitivity and reduce false-negative outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this research, a deep learning-based approach for binary classification of breast cancer into benign and malignant categories utilizing histopathological images is presented. A dataset comprising 10,000 high-resolution histopathology images was used to evaluate the execution of two vision models: Swin Transformer V2 and ConvNeXt V2. Swin Transformer V2, a progressive vision transformer with shifted window self-attention, and ConvNeXt V2, a modern convolutional neural network motivated by transformer plans, were fine-tuned and tested for their adequacy in feature representation and classification accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The experimental results demonstrate that Swin Transformer V2 consistently outperforms ConvNeXt V2 across all evaluation metrics, achieving a peak classification accuracy of 0.985, which reflects its superior capability in capturing subtle morphological and contextual variations in histopathological tissues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The attention-driven global feature modeling in Swin Transformer V2 enables more discriminative representations compared to convolutional inductive biases, particularly for complex cellular patterns. These findings suggest that transformer-based architectures offer significant advantages over modern CNNs for histopathological breast cancer classification, and they hold substantial potential for advancing computer-aided diagnosis systems in digital pathology. The comparative insights provided in this study can guide the selection of robust deep learning models for scalable and reliable clinical decision-support systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1770667","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T12:51:41Z","timestamp":1773924701000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Transformers meet CNNs for insights into breast mass classification from histopathological images"],"prefix":"10.3389","volume":"9","author":[{"given":"Vatsala","family":"Anand","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Akal University","place":["Bathinda, Punjab, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajay","family":"Khajuria","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Akal University","place":["Bathinda, Punjab, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"36","DOI":"10.52866\/2788-7421.1309","article-title":"MEMF-net: a mega-ensemble of multi-feature CNNs for classification of breast histopathological images","volume":"6","author":"Abdulaal","year":"2025","journal-title":"Iraqi J. 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