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December 10, 2025TomographyOpen Access

Clinically Focused Computer-Aided Diagnosis for Breast Cancer Using SE and CBAM with Multi-Head Attention

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Authors

ZÖZeki ÖğütMKMücahit KaradumanMYMuhammed Yıldırım

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Overview

Deep learning-based model demonstrates 99.55% accuracy in diagnosing breast cancer from ultrasound images, indicating improved diagnostic accuracy for patient management.

Key Points

  • Achieved 99.55% accuracy in classifying malignant and benign breast tissues from ultrasound images, indicating effective diagnostic potential.
  • Deep learning-based model trained on ultrasound images, highlighting high diagnostic accuracy compared to conventional methods.
  • Model effectively combines Squeeze-and-Excitation and Convolutional Block Attention mechanisms to enhance spatial information processing.
  • Results suggest this approach may enable faster and more reliable decision support in clinical applications for breast cancer.

Cite This Study

Öğüt et al. (2025) studied this question.

synapsesocial.com/papers/69401b372d562116f28f7ea5https://doi.org/10.3390/tomography11120138
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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study2025
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  4. 4Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors using Segmentation and Feature Extraction2024
  5. 5Computer-aided diagnosis system for breast ultrasound images using deep learning2019 · 136 citations