Laser & Optoelectronics Progress, Volume. 62, Issue 10, 1017001(2025)
Discriminant Classification of Liver Tumors in Nude Mice by Laser-Induced Breakdown Spectroscopy Combined with Machine Learning
Figures & Tables(7)
Fig. 3. LIBS wavelet transform full spectra after noise reduction. (a) Liver tumor tissue; (b) adjacent muscle tissue
Fig. 4. Comparison of local LIBS of some elements of liver tumor and adjacent muscle tissue after wavelet denoising. (a) Mg; (b) Ca; (c) K
Fig. 5. Pathological H&E staining of liver tumor and adjacent muscle tissue of nude mice and distribution of some elements drawn by LIBS. (a) (e) H&E stained images; (b) (f) Ca element; (c) (g) K element; (d) (h) Mg element
Fig. 6. Results of PCA feature extraction. (a) Scatter plots of liver tumor and muscle tissue spectral data in the first three principal components; (b) the relationship between the number of principal components and the cumulative contribution rate
Table 1. Comparison of modeling results with or without feature extraction
View tableTable 1. Comparison of modeling results with or without feature extraction
Feature extraction Model Average classification accuracy /% Mean modeling time /s Full spectrum data SVM 100 7.46 FDA 100 30.23 KELM 97.5 1.00 RF 100 54.63 Bayes 97.5 35.76 PCA SVM 100 0.29 FDA 100 0.26 KELM 100 0.04 RF 100 0.13 Bayes 100 0.11
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Yingjie Peng, Qianlin Lian, Yue Ma, Xiaohan Nie, Jianjun Chen. Discriminant Classification of Liver Tumors in Nude Mice by Laser-Induced Breakdown Spectroscopy Combined with Machine Learning[J]. Laser & Optoelectronics Progress, 2025, 62(10): 1017001
Paper Information
Category: Medical Optics and Biotechnology
Received: Nov. 13, 2024
Accepted: Feb. 12, 2025
Published Online: Apr. 23, 2025
The Author Email: Jianjun Chen (cjjliyan@163.com)
CSTR:32186.14.LOP242259
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