Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method

被引：31

作者：

Li, Yuanpeng ^{[1
,2
]}

Deng, Liangyu ^{[1
]}

Yang, Xinhao ^{[1
]}

Liu, Zhao ^{[3
]}

Zhao, Xiaoping ^{[3
]}

Huang, Furong ^{[1
]}

Zhu, Siqi ^{[1
]}

Chen, Xingdan ^{[1
]}

Chen, Zhenqiang ^{[1
]}

Zhang, Weimin ^{[3
]}

机构：

[1] Jinan Univ, Dept Optoelect Engn, Guangdong Prov Key Lab Opt Fiber Sensing & Commun, Guangzhou 510632, Guangdong, Peoples R China

[2] Guangxi Normal Univ, Coll Phys Sci & Technol, Guangxi 541004, Guilin, Peoples R China

[3] 74th Grp Army Hosp Peoples Liberat Army, Dept Gastroenterol & Endocrinol, Guangzhou 510318, Guangdong, Peoples R China

来源：

BIOMEDICAL OPTICS EXPRESS | 2019年 / 10卷 / 10期

基金：

中国国家自然科学基金;

关键词：

HELICOBACTER-PYLORI; RNA; CHROMOENDOSCOPY; BIOMARKER; MARKER;

D O I：

10.1364/BOE.10.004999

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the "spectral + spatial" features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

引用

页码：4999 / 5014

页数：16

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