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Proceedings Paper

Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network
Author(s): Jing Yang; Cheng Wang; Gan Cai; Xiaona Dong
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Paper Abstract

The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

Paper Details

Date Published: 31 October 2016
PDF: 9 pages
Proc. SPIE 10024, Optics in Health Care and Biomedical Optics VII, 1002439 (31 October 2016); doi: 10.1117/12.2246070
Show Author Affiliations
Jing Yang, Univ. of Shanghai for Science and Technology (China)
Cheng Wang, Univ. of Shanghai for Science and Technology (China)
Gan Cai, Univ. of Shanghai for Science and Technology (China)
Xiaona Dong, Univ. of Shanghai for Science and Technology (China)

Published in SPIE Proceedings Vol. 10024:
Optics in Health Care and Biomedical Optics VII
Qingming Luo; Xingde Li; Ying Gu; Yuguo Tang, Editor(s)

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