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. 2021 Sep 3;12(21):6473–6483. doi: 10.7150/jca.63879

Figure 2.

Figure 2

Identification of cancer lesions by the ResNet50-PSPNet semantic segmentation algorithm. A. The network of ResNet50-PSPNet structure. ResNet50 was used as CNN backbone for feature extraction, and PSPNet for semantic segmentation. In the output picture, the predictive region was highlighted as red. B. The true-positive cases were accurately predicted and highlighted as red. C. When gastric mucosa was attached by blood clot (left) or metal clip (right), the computer was confused with false-positive prediction. The red highlighted regions showed the predicted results. The blue boxes labeled true-positive regions, and the yellow boxes labeled the false-positive regions. D. Both failed predictions were early gastric cancers.