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Proceedings Paper

Comparison testing of machine learning algorithms separability on Raman spectra of skin cancer
Author(s): Kirill A. Serzhantov; Oleg O. Myakinin; Mariya G. Lisovskaya; Ivan A. Bratchenko; Alexander A. Moryatov; Sergey V. Kozlov; Valery P. Zakharov
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Paper Abstract

The aim of this research is maximizing differentiation quality of skin neoplasms by Raman Spectroscopy (RS) and Autofluorescence (AF) using conventional Machine Learning (ML) algorithms (which means excluding Neural Networks). Thus, a basic task of this research consists of making and testing ML algorithm ensembles on Raman spectra, that were obtained in vivo in Samara Oncology Clinical Center. The data (spectra) have been obtained in a form of text files containing the identifier of a patient, as well as a Raman spectrum in the form of pair values – a wavelength and an appropriate value. All data (964 spectra) has been divided for two classes: Tumor and Skin. Further preprocessing of the input data and the analysis of models of machine learning for a problem of classification have been carried out. We used the following ML tools, namely Python 3.7.3, an open source ML libraries Scikit-learn v0.21.2, NumPy 1.17.0, and Pandas 1.0.1, IDE Anaconda Enterprise 5.3 and cloud service Google Colaboratory with an interactive environment Jupyter Notebook. Machine learning models that show a high accuracy result include Classification and Regression Tree (CART), Support Vector Classification (SVC), Logistic Regression (LR), K-nearest neighbors algorithm (KNN). These classifiers show high quality classification on standard parameters already. The Soft Voting Classification module was selected as an ensemble, that allows us to use several models of classifiers that are not similar to each other at once, combine them into one classifier. Results of this ensemble testing from these ML algorithms showed that the classification accuracy, unlike the best qualifier, has not been increased. However, the metrics of classification quality show that the model has become stabler and steady. Results: specificity - 93%, sensitivity - 88, harmonic mean between precision and recall (F1 score) - 90%. An analysis of validation and training curves indicated a small size of training data and, for some cases, a high complexity of the model, which led to a decrease in the classification accuracy.

Paper Details

Date Published: 13 April 2020
PDF: 7 pages
Proc. SPIE 11359, Biomedical Spectroscopy, Microscopy, and Imaging, 1135906 (13 April 2020); doi: 10.1117/12.2555639
Show Author Affiliations
Kirill A. Serzhantov, Togliatti State Univ. (Russian Federation)
Oleg O. Myakinin, Samara Univ. (Russian Federation)
Mariya G. Lisovskaya, Togliatti State Univ. (Russian Federation)
Samara Univ. (Russian Federation)
Ivan A. Bratchenko, Samara Univ. (Russian Federation)
Alexander A. Moryatov, Samara State Medical Univ. (Russian Federation)
Sergey V. Kozlov, Samara State Medical Univ. (Russian Federation)
Valery P. Zakharov, Samara Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 11359:
Biomedical Spectroscopy, Microscopy, and Imaging
Jürgen Popp; Csilla Gergely, Editor(s)

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