Research on Liver Cancer Pathology Image Recognition Based on Deep Learning Image Processing

The accurate diagnosis of lung cancer through pathological image analysis remains challenging due to the complexity and heterogeneity of histopathological features. Current deep learning approaches often fail to comprehensively capture the diverse visual characteristics present in tissue samples, limiting their diagnostic accuracy. 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. 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. 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.