Open AccessEarly diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method
Author(s) -
Yuanpeng Li,
Liangyu Deng,
Xinhao Yang,
Zhao Liu,
Xiaoping Zhao,
Furong Huang,
Siqi Zhu,
Xingdan Chen,
Zhenqiang Chen,
Weimin Zhang
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.004999
Subject(s) - precancerous lesion , hyperspectral imaging , cancer , artificial intelligence , spectral imaging , pattern recognition (psychology) , lesion , medicine , clinical diagnosis , radiology , computer science , pathology , remote sensing , clinical psychology , geology
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.