ArticleLasers in medical science2018
Identification of cervical cancer using laser-induced breakdown spectroscopy coupled with principal component analysis and support vector machine.
Article in Lasers in medical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From Fundamentals of Laser-Induced Breakdown Spectroscopy to Recent Advancements in Cancer Detection and Calcified Tissues Analysis: An Overview (2015-2025).Molecules (Basel, Switzerland) · 2025Review
- Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning.Scientific reports · 2023Article
- Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis.Biomedical optics express · 2021Article
- Primary study of identification of parathyroid gland based on laser-induced breakdown spectroscopy.Biomedical optics express · 2021Article
- DeepConsensus: Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction.Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks · 2020Article
- NmSEER V2.0: a prediction tool for 2'-O-methylation sites based on random forest and multi-encoding combination.BMC bioinformatics · 2019Article
- Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.American journal of obstetrics and gynecology · 2019Article
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8 authors.
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Abstract
Cervical cancer is one of the most widespread diseases in women. Traditional cancer diagnosis is extremely complicated and relies on subjective interpretation of biopsy material. In this work, laser-induced breakdown spectroscopy (LIBS) was used in cervical cancer recognition. In order to improve identification accuracy of cervical cancer by LIBS, the chemometric methods of principal component analysis (PCA) and support vector machine (SVM) were combined. The results show that the content of trace elements in normal tissues and cervical cancer tissues was significantly different. Normalized peak intensities of Na, Mg, and K in the cervical cancer tissues were significantly higher than normal tissues, and the normalized peak intensities of Ca in the normal tissues were higher than cervical cancer tissues. The identification accuracies of PCA-SVM are better than SVM, with the achieved accuracies of 94.44% and 93.06%, respectively. It can be concluded that LIBS techniques coupled with chemometric method is a potential in cancer tissue identification, which provides a preliminary research basis for real-time diagnosis of cancer tissues using LIBS.
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