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The proposed pipeline combines patient-level data partitioning before augmentation, a two-stage transfer-learning strategy based on ResNet50, and an Inter-View Attention Fusion (IVAF) module for adaptive fusion of paired craniocaudal (CC) and mediolateral oblique (MLO) feature maps. IVAF was modeled as a light-weighted convolutional gating strategy added after the last ResNet50 convolutional layer in order to create a weighted spatial-channel representation from the paired mammography images. In terms of the performance of the model under CBIS-DDSM held-out testing protocol, the entire model scored an accuracy of 97.12%, sensitivity of 96.44%, specificity of 97.68%, and AUC-ROC of 0.9876 based on the test results obtained on 6,117 images of 222 different patients. The average accuracy obtained using 100 random seeds was found to be 97.11% \u00b1 0.18%.<\/jats:p>","DOI":"10.1038\/s41598-026-62255-2","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T05:55:45Z","timestamp":1784267745000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Breast cancer detection and classification via a robust deep learning approach"],"prefix":"10.1038","volume":"16","author":[{"given":"Magy","family":"Makram","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alber S.","family":"Aziz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary Monir","family":"Saeid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa Thabet","family":"Mohamed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"62255_CR1","unstructured":"International Agency for Research on Cancer. 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