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September 5, 2025Open Access

A Hybrid CNN-Transformer Deep Learning Model for Differentiating Benign and Malignant Breast Tumors Using Multi-View Ultrasound Images

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Authors

张Q张麒 ZHANG QiJZJianxing ZhangChinese Academy of SciencesPTPan TangGuangzhou University of Chinese Medicine

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Overview

This analysis finds that a hybrid deep learning model improves breast cancer diagnosis in ultrasound images, suggesting enhanced accuracy through multi-view assessment.

Key Points

  • The hybrid CNN-Transformer model achieved a diagnostic accuracy of 93% on both internal and external test sets, indicating its robustness.
  • Sensitive detection of malignant lesions was significant, with a sensitivity of 92% internally and 94% externally, enhancing early breast cancer diagnosis.
  • Observational analysis across multiple ultrasound images showed the model outperformed a baseline single-image model, emphasizing better diagnostic capabilities.
  • This approach may enable more reliable and accurate breast cancer diagnostics, showcasing the strength of multi-view information fusion.

Cite This Study

Qi et al. (2025) studied this question.

synapsesocial.com/papers/68bb3a352b87ece8dc954c64https://doi.org/10.1101/2025.08.24.25334030
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Hybrid CNN-Transformer Deep Learning Model for Differentiating Benign and Malignant Breast Tumors Using Multi-View Ultrasound Images.2026
  2. 2A Hybrid Model for Ultrasound Image-Based Breast Cancer Diagnosis Using EfficientNet-V2 and Vision Transformer2026
  3. 3A Multi-Task Transformer With Local-Global Feature Interaction and Multiple Tumoral Region Guidance for Breast Cancer Diagnosis2024 · 21 citations
  4. 4An Interpretable Breast Ultrasound Image Classification Algorithm Based on Convolutional Neural Network and Transformer2024 · 18 citations
  5. 5Computer-aided diagnosis system for breast ultrasound images using deep learning2019 · 136 citations