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

Yuanpeng Li, Liangyu Deng, Xinhao Yang, Zhao Liu, Xiaoping Zhao, Furong Huang, Siqi Zhu, Xingdan Chen, Zhenqiang Chen, Weimin Zhang, Yuanpeng Li, Liangyu Deng, Xinhao Yang, Zhao Liu, Xiaoping Zhao, Furong Huang, Siqi Zhu, Xingdan Chen, Zhenqiang Chen, Weimin Zhang

Abstract

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.

Conflict of interest statement

The authors declare that there are no conflicts of interest related to this article.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

Figures

Fig. 1.
Fig. 1.
Fluorescence hyperspectral imaging system.
Fig. 2.
Fig. 2.
Schematic diagram of residual network
Fig. 3.
Fig. 3.
Schematic diagram of spectral acquisition: (A) original fluorescence image; (B) binarized image; and (C) average spectrum by normalization of each tissue.
Fig. 4.
Fig. 4.
Average spectrum of normal, atrophic gastritis, intestinal metaplasia, and gastric cancer: (A) average spectrum of each tissue with error bars and (B) average spectral intensity of each tissue at 496 nm, 546 nm, and 670 nm.
Fig. 5.
Fig. 5.
Second-derivative spectra of nonprecancerous lesions, precancerous lesions, and gastric cancer: (A) average spectrum of second derivative and (B) average spectrum of second-derivative with error bars.
Fig. 6.
Fig. 6.
Schematic diagram of spatiospectral preprocessing.
Fig. 7.
Fig. 7.
Image of nonprecancerous lesions, precancerous lesions, and gastric cancer: Figs.7(A)–(C) histopathological image; Figs.7(D)–(F) original fluorescent image; and Figs.7(G)–(I) spatiospectral image.
Fig. 8.
Fig. 8.
Schematic diagram of Resnet34 for early diagnosis of gastric cancer: (A) image dataset by using spatiospectral preprocessing; (B) convolutional layer and residual module; and (C) fully connected layer and softmax multiclassifier.
Fig. 9.
Fig. 9.
Modeling results of Resnet34: (A) accuracy rate varies with number of iterations and (B) curve of loss function varies with number of iterations.
Fig. 10.
Fig. 10.
Model hyperparameter optimization results.

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