NONE 20260302135757450 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 1109-9518 2224-2902 Christian Kutabuna Kubakisa Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF THE CONGO Witesyavwirwa Vianney Kambale Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria, SOUTH AFRICA Isaac Lukusa Kayembe Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF THE CONGO Mahmoud Hamed Institute for Smart Systems Technologies, Universität Klagenfurt, AUSTRIA Divin Kayeye Kabeya Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF THE CONGO Kyandoghere Kyamakya Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF THE CONGO Early and reliable breast cancer diagnosis remains a public-health priority, yet routine screening still suffers from inter-reader variability, heavy workload, and uneven resource availability. To move beyond generic claims of “deep learning works,” this paper reports a transparent, like-for-like comparison of three families of models under a single protocol and fixed pre-processing: a custom Convolutional Neural Network (CNN), MobileNetV3, and a Vision Transformer (ViT-L16). We evaluate on two complementary benchmarks—MIAS mammography (n=322 images) and BreakHis histopathology (n=7,909 tiles)—covering both binary (benign vs. malignant/abnormal) and multiclass settings. All models are trained on 224×224 inputs with harmonized augmentation; MIAS splits are image-level, whereas BreakHis splits are patient-level to avoid leakage. On MIAS, MobileNetV3 yields the strongest results with 93.25% accuracy for binary detection and 92.00% for multiclass classification, surpassing both the bespoke CNN and ViT-L16 under identical conditions. On BreakHis, ViT-L16 achieves the best validation performance (91.62% binary; 90.28% multiclass), which is consistent with the advantage of long-range self-attention for texture-rich histology. A side-by-side reading against recent literature shows our scores to be competitive and, in several cases, state-of-the-art under comparable validation protocols. Beyond headline numbers, we discuss deployment trade-offs: MobileNetV3 offers a favorable accuracy–efficiency balance for embedded or resource-constrained settings, while ViT-L16 is better suited to centralized, high-capacity pipelines. 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