Automatic recognition of tumour-infiltrating lymphocytes in pathological biopsy images of the gastric mucosa

Figure 5 Ablation experiment results of the gastric tumour-infiltrating lymphocyte-convolutional neural network model. A: Compared with equal weight fusion, dynamic learning weight fusion achieves higher accuracy (99.2% vs 96.5%) and kappa (0.98 vs 0.89, P < 0.001), with better intestinal metaplasia tumour-infiltrating lymphocyte recognition (98.6% vs 94.2%); B: 2-channel haematoxylin/eosin input outperforms 3-channel red green blue input in terms of accuracy (99.2% vs 94.7%), specificity (100.0% vs 92.3%) and sensitivity (98.7% vs 95.1%, P < 0.001), eliminating staining batch interference and improving multicentre sample robustness. H/E: Haematoxylin/eosin; RGB: Red green blue.