Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis
| Autor: | M. Nouman Khan, Kai Wei, Qianqian Wang, Geer Teng, Xiangjun Xu, Bushra Sana Idrees, Guoyan Chen, Xutai Cui |
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| Rok vydání: | 2021 |
| Předmět: |
0303 health sciences
Computer science business.industry Supervised learning Pattern recognition 01 natural sciences Article Atomic and Molecular Physics and Optics k-nearest neighbors algorithm Random forest 010309 optics Support vector machine 03 medical and health sciences Similarity (network science) 0103 physical sciences Principal component analysis Feature (machine learning) Artificial intelligence Cluster analysis business 030304 developmental biology Biotechnology |
| Zdroj: | Biomed Opt Express |
| ISSN: | 2156-7085 |
| DOI: | 10.1364/boe.431356 |
| Popis: | Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models. |
| Databáze: | OpenAIRE |
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