Application of convolutional neural network in detecting and classifying gastric cancer

Table 3 Detailed information on studies concerning prediction of depth of tumor invasion by convolutional neural network in gastric cancer

Ref.	Dataset	Resolution	Sensitivity %	Specificity %	Accuracy/AUC %	PPV %	NPV %
Zhu et al[11] (2019)	Development datasets: 5056; Validation datasets: 1264; Test dataset: 203	299 × 299	76.47	95.56	89.16	89.66	88.97
Yoon et al[32] (2019)	11539 images were randomly organized into five different folds, and at each fold, the training: validation: testing dataset ratio was 3:1:1	NA	79.2	77.8	85.1	79.3	77.7
Zheng et al[34] (2020)	Totally 5855, training:verification dataset ratio was 4:1	512 × 557	NA	NA	T2 stage: 90; T3 stage: 93; T4 stage: 95	NA	NA

AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; NA: Not applicable.