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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Comparative evaluation of transformer and CNN architectures for breast cancer detection across histopathology, mammography, and ultrasound

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pp. 1613–1621Vol. 47Issue 5-AMay 2026DOI: 10.47974/JIOS-2251XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2251
Pages:
1613–1621

Abstract

Automated analysis of images in various imaging modalities are beneficial in breast cancer screening and diagnosis. This paper compares four deep learning models Vision Transformer (ViT), EfficientNetV2 with CBAM, ConvMixer, and MobileNetV4 on three publicly available datasets of breast imaging (histopathology, mammography, ultrasound). We measure accuracy, recall, F1-score, computational cost, and suitability to modality. To underline the focus on the reproducibility and modality-specific analysis, the methodology underlines concise descriptions of every model in terms of mathematical intuition, architecture illustration, algorithmic steps. We have found that attention-augmented CNNs (CBAM-EfficientNetV2) perform well in terms of accuracy and robustness across modalities and MobileNetV4 has a high efficiency-accuracy trade-off to be deployed on resource-constrained devices.

Keywords

Breast cancer detectionVision transformerCBAM-EfficientNetV2ConvMixerMobileNetV4HistopathologyMammographyUltrasound

Subject Classifications

03G1011T71

References

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