Tumor tissue classification based on micro-hyperspectral technology and deep learning

Bingliang Hu; Jian Du; Zhoufeng Zhang; Quan Wang

doi:10.1364/BOE.10.006370

Tumor tissue classification based on micro-hyperspectral technology and deep learning

Biomed Opt Express. 2019 Nov 19;10(12):6370-6389. doi: 10.1364/BOE.10.006370. eCollection 2019 Dec 1.

Authors

Bingliang Hu^{1

2}, Jian Du^{1

2}, Zhoufeng Zhang^{1

2}, Quan Wang^{1

2}

Affiliations

¹ Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi 710119, China.
² Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an, Shaanxi 710119, China.

Abstract

In order to explore the application of hyperspectral technology in the pathological diagnosis of tumor tissue, we used microscopic hyperspectral imaging technology to establish a hyperspectral database of 30 patients with gastric cancer. Based on the difference in spectral-spatial features between gastric cancer tissue and normal tissue in the wavelength of 410-910 nm, we propose a deep-learning model-based analysis method for gastric cancer tissue. The microscopic hyperspectral feature and individual difference of gastric tissue, spatial-spectral joint feature and medical contact are studied. The experimental results show that the classification accuracy of proposed model for cancerous and normal gastric tissue is 97.57%, the sensitivity and specificity of gastric cancer tissue are 97.19% and 97.96% respectively. Compared with the shallow learning method, CNN can fully extract the deep spectral-spatial features of tumor tissue. The combination of deep learning model and micro-spectral analysis provides new ideas for the research of medical pathology.