TABLE 2.

The comparison of predictive ResNet50 model's performance across previous studies.

Authors	Models	No. of original images	Number of classes	Sensitivity (%)	Specificity (%)	Accuracy (%)
Present study	CNN‐ResNet50 (Testing phase)	non‐FLL = 150	5	92.7 ± 7.2(non‐FLL)	89.3 ± 4.6(non‐FLL)	87.2 ± 2.2
		Cyst = 150		92.7 ± 2.8 (Cyst)	98.6 ± 2(Cyst)
		FFS = 77		88.7 ± 6.9 (FFS)	81.6 ± 5.8(FFS)
		HMG = 150		81.3 ± 3.8 (HMG)	86.3 ± 7.6(HMG)
		HCC = 54		80.7 ± 6.8 (HCC)	81.2 ± 3.7 (HCC)
Hwang et al. (2015) 20	Two‐layered feed‐forward neural network (FFNN)	Cyst = 29 HMG = 37 Malignancy (HCC+MLC) = 33	2(Cyst vs. HMG, Cyst vs.Malignancy, HMG vs. Malignancy)	98 (Cyst vs. HMG) 97 (Cyst vs. Malignancy) 40 (HMG vs. Malignancy)	98 (Cyst vs. HMG) 98 (Cyst vs. Malignancy) 60 (HMG vs. Malignancy)	98 (Cyst vs. HMG) 98 (Cyst vs. Malignancy) 51 (HMG vs. Malignancy)
Reddy et al. (2018) 21	Model1: CNN Model2: VGG16+transfer learning Model 3: VGG16+transfer learning+fine tuning	Normal liver = 64 and Fatty liver = 93	2	89 (1st model) 95 (2nd model) 95 (3rd model)	85 (1st model) 76 (2nd model) 85 (3rd model)	84 (1st model) 88 (2nd model) 91 (3rd model)
Yamakawa et al. (2019) 22	CNN‐based VGGnet	Cyst = 159 HMG = 68 HCC = 73 MLC = 24	4	98 (Cyst) 87 (HMG) 86(HCC) 46 (MLC)	12	88
			2 (Benign vs. Malignancy)	94 (Malignancy)	5	91
Ryu et al. (2021) 23	CNN+ReLU	Cyst = 1214 HMG = 1220 HCC = 874 MLC = 1001	4	94 (Cyst) 83 (HMG) 67 (HCC) 82 (MLC)	90	80
			2 (Benign vs. Malignancy)	87 (Malignancy)	89	90
Nishida et al. (2022) 24	CNN‐based VGGNet (Model 3, utilizing the largest available HCC dataset)	HCC = 1750 MLC = 396 HMG = 433 Cyst = 43	4	67.5 (HCC)	96 (HCC)	93.4 (HCC)

Abbreviations: CCN, convolution neural network; Cyst, simple hepatic cyst; FFS, fat focal sparing; HCC, hepatocellular carcinoma; HMG, hemangioma; MLC, metastases liver cancer; non‐FLL, non‐focal liver lesion.