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<p>The fast development of computing and human–machine interaction technologies have enabled the computer-aided diagnosis (CAD) of lung diseases. Though significant progress has been made in recent years, CAD still faces the challenge of eliminating or minimizing the misdiagnosis. To this end, inspired by the diagnostic practices of radiologists, we present a novel method named “SACFNet” for pulmonary nodules detection in this paper, which integrates Spatial Attention and Channel Feature Fusion Network into the 3D convolutional neural network(CNN). Specifically, our approach incorporates a dual-branch spatial enhancement module and a multi-scale semantic feature fusion module to enhance the spatio features of lung nodule data and improve attention to global information. And design a multi-scale feature enhancement module to increase multi-scale semantic information. The experimental results indicate that the devised SACFNet is accurate in identifying lung nodules in CT scans, with an average Free-Response Receiver Operating Characteristic(FROC) score of 90.98% on the LUNA16 dataset.</p>

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SACFNet: Spatial Attention and Channel Feature Fusion Network for Pulmonary Nodules Detection

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Linsong Zhang,
  • Muwei Jian,
  • Jianbin Du,
  • Xiaoguang Li,
  • Feng Xu,
  • Hui Yu

摘要

The fast development of computing and human–machine interaction technologies have enabled the computer-aided diagnosis (CAD) of lung diseases. Though significant progress has been made in recent years, CAD still faces the challenge of eliminating or minimizing the misdiagnosis. To this end, inspired by the diagnostic practices of radiologists, we present a novel method named “SACFNet” for pulmonary nodules detection in this paper, which integrates Spatial Attention and Channel Feature Fusion Network into the 3D convolutional neural network(CNN). Specifically, our approach incorporates a dual-branch spatial enhancement module and a multi-scale semantic feature fusion module to enhance the spatio features of lung nodule data and improve attention to global information. And design a multi-scale feature enhancement module to increase multi-scale semantic information. The experimental results indicate that the devised SACFNet is accurate in identifying lung nodules in CT scans, with an average Free-Response Receiver Operating Characteristic(FROC) score of 90.98% on the LUNA16 dataset.