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Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial‐least‐square discriminant analysis and support vector machine
Author(s) -
Li Yuanpeng,
Xie Xiaojuan,
Yang Xinhao,
Guo Liu,
Liu Zhao,
Zhao Xiaoping,
Luo Ying,
Jia Wei,
Huang Furong,
Zhu Siqi,
Chen Zhenqiang,
Chen Xingdan,
Wei Zhong,
Zhang Weimin
Publication year - 2019
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201800324
Subject(s) - hyperspectral imaging , cancer , support vector machine , linear discriminant analysis , stage (stratigraphy) , pattern recognition (psychology) , artificial intelligence , cancer detection , medicine , pathology , computer science , biology , paleontology
This study investigated the feasibility of using fluorescence hyperspectral imaging technology to diagnose of early‐stage gastric cancer. Fluorescence spectral images of 76 patients who were pathologically diagnosed as non‐atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100‐pixel points were randomly extracted after binarization. Diagnostic models of non‐atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial‐least‐square discriminant analysis (PLS‐DA) and support vector machine (SVM) algorithms. The prediction effects of PLS‐DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670 nm, and regular changes in fluorescence intensity at 546 nm were in the following order: normal > precancerous lesions > gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS‐DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early‐stage gastric cancer.