Application of convolutional neural network in detecting and classifying gastric cancer

Table 2 Detailed information on studies concerning histological classification by convolutional neural network in gastric cancer

Ref.	Training dataset	Test dataset	Resolution	Group	AUC %
Cho et al[25] (2019)	4205	812	1280 × 640	Five-category classification	84.6
				Cancer vs non-cancer	87.7
				Neoplasm vs non-neoplasm	92.7
Sharma et al[27] (2017)	231000 for cancer classification	NA	512 × 512	Cancer classification	69.9
	47130 for necrosis detection			Necrosis detection	81.4
Iizuka et al[28] (2020)	3628	500	512 × 512	Adenocarcinoma	98
				Adenoma	93.6
Song et al[29] (2020)	2123	3212 from PLAGH	320 × 320	Benign and malignant cases and tumour subtypes	98.6
		595 from PUMCH			99.0
		987 from CHCAMS			99.6

PLAGH: Chinese PLA General Hospital; PUMCH: Peking Union Medical College Hospital; CHCAMS: Cancer Hospital, Chinese Academy of Medical Sciences; AUC: Area under the curve.