Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastroenterol. Jun 14, 2022; 28(22): 2457-2467
Published online Jun 14, 2022. doi: 10.3748/wjg.v28.i22.2457

Table 6 Results of proposed network in esophageal lesion database, %

	ACC	SENS	SPEC
Lesions invading the submucosa and muscularis mucosae	77.11	84.5	93.6
Lesions invading the muscularis propria	73.4	86.69	78.52
Lesions invading the serosa	98.9	72	99.14
Lesions from the muscularis mucosa	78.64	76.32	90.62
Lesions from the muscularis propria	84.4	81.64	90.92
Average	82.49	80.23	90.56

SENS: Sensitivity; SPEC: Specificity; ACC: Accuracy.

Citation: Liu GS, Huang PY, Wen ML, Zhuang SS, Hua J, He XP. Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network. World J Gastroenterol 2022; 28(22): 2457-2467
URL: https://www.wjgnet.com/1007-9327/full/v28/i22/2457.htm
DOI: https://dx.doi.org/10.3748/wjg.v28.i22.2457

Liu GS, Huang PY, Wen ML, Zhuang SS, Hua J, He XP. Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network. World J Gastroenterol 2022; 28(22): 2457-2467 [PMID: 35979257 DOI: 10.3748/wjg.v28.i22.2457]

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