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<p>The accurate diagnosis of Liver cancer through pathological image analysis remains challenging due to the complexity and heterogeneity of histopathological features. This study proposes MSAF-Net, a novel multi-space attention fusion network that systematically integrates five complementary feature spaces (R, B, Y, entropy, and LBP) with an SE-block enhanced fusion mechanism and EfficientNet-Lite based feature extraction. The proposed framework establishes a new state-of-the-art in pathological image analysis by effectively combining engineered feature spaces with deep learning, offering both high diagnostic reliability and computational efficiency for clinical applications. Experimental results demonstrate superior performance with 94.7% accuracy, 93.2% sensitivity, and 95.8% specificity, representing significant improvements of 6.3%, 7.1%, and 5.6% respectively over conventional single-space methods.</p>

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Research on liver cancer pathology image recognition based on deep learning image processing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月2日

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  • Mingyuan Yang,
  • Huanzhang Niu

摘要

The accurate diagnosis of Liver cancer through pathological image analysis remains challenging due to the complexity and heterogeneity of histopathological features. This study proposes MSAF-Net, a novel multi-space attention fusion network that systematically integrates five complementary feature spaces (R, B, Y, entropy, and LBP) with an SE-block enhanced fusion mechanism and EfficientNet-Lite based feature extraction. The proposed framework establishes a new state-of-the-art in pathological image analysis by effectively combining engineered feature spaces with deep learning, offering both high diagnostic reliability and computational efficiency for clinical applications. Experimental results demonstrate superior performance with 94.7% accuracy, 93.2% sensitivity, and 95.8% specificity, representing significant improvements of 6.3%, 7.1%, and 5.6% respectively over conventional single-space methods.