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<p>Many studies based on convolutional neural networks (CNNs) for breast cancer axillary lymph node (ALN) images have focused on large sample analysis and clinical parameter integration, while limited attention has been paid to lightweight models for small ALN datasets. In this paper, we have selected a small number of ALN ultrasound image datasets as the research subject and designed a TX-GGCA model, consisting of the Tiny-Xception model and the global grouping coordinate attention (GGCA). The TX-GGCA demonstrated an accuracy of 99.14% and an area under curve (<i>AUC</i>) of 0.999 7 in classifying normal and abnormal ALN images, outperforming the best traditional model (accuracy: 95.69%, <i>AUC</i>: 0.993 2). It showed the potential value of this model for clinical diagnosis in primary hospitals with limited sample sizes.</p>

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TX-GGCA: a lightweight model based on Tiny-Xception for predicting axillary lymph node metastasis

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Wenyuan Zeng,
  • Fengnian Liu,
  • Miduo Tan,
  • Yibo Zhang,
  • Jing Long,
  • Lin Tang

摘要

Many studies based on convolutional neural networks (CNNs) for breast cancer axillary lymph node (ALN) images have focused on large sample analysis and clinical parameter integration, while limited attention has been paid to lightweight models for small ALN datasets. In this paper, we have selected a small number of ALN ultrasound image datasets as the research subject and designed a TX-GGCA model, consisting of the Tiny-Xception model and the global grouping coordinate attention (GGCA). The TX-GGCA demonstrated an accuracy of 99.14% and an area under curve (AUC) of 0.999 7 in classifying normal and abnormal ALN images, outperforming the best traditional model (accuracy: 95.69%, AUC: 0.993 2). It showed the potential value of this model for clinical diagnosis in primary hospitals with limited sample sizes.