Application of artificial intelligence based on contrast-enhanced CT imaging for predicting peritoneal metastasis in patients with T3/T4 stage gastric cancer
Fig 5
ROC-AUC of various models at different dropout probabilities on test set.
(A) Figure A shows the ROC-AUC values of different convolutional neural network models at dropout probabilities of 0 and 0.3; (B) Figure B displays the ROC-AUC values of the Inception-ResNetV2 model with various attention mechanisms integrated at dropout probabilities of 0 and 0.3. ROC-AUC, Receiver Operating Characteristic Area Under the Curve.