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Diagnostics
Volume 7
Issue 1
10.3390/diagnostics7010009
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Article

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

by
Carlos E. Galván-Tejada
1,
Laura A. Zanella-Calzada
2,
Jorge I. Galván-Tejada
1,
José M. Celaya-Padilla
3,*,
Hamurabi Gamboa-Rosales
1,
Idalia Garza-Veloz
4 and
Margarita L. Martinez-Fierro
4
1
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico
2
Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Lateral Av. Salvador Nava s/n., 78290 San Luis Potosí, SLP, Mexico
3
CONACYT – Universidad Autónoma de Zacatecas – Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico
4
Unidad Académica de Medicina Humana y Ciencias de la Salud, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Tanya W. Moseley
Diagnostics 2017, 7(1), 9; https://doi.org/10.3390/diagnostics7010009
Received: 14 December 2016 / Revised: 17 January 2017 / Accepted: 9 February 2017 / Published: 14 February 2017
(This article belongs to the Special Issue Breast Imaging)
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Abstract

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. View Full-Text
Keywords: breast cancer; mammography image features; mammography descriptors; CAD; multivariate model; genetic algorithm; machine learning algorithms breast cancer; mammography image features; mammography descriptors; CAD; multivariate model; genetic algorithm; machine learning algorithms
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MDPI and ACS Style

Galván-Tejada, C.E.; Zanella-Calzada, L.A.; Galván-Tejada, J.I.; Celaya-Padilla, J.M.; Gamboa-Rosales, H.; Garza-Veloz, I.; Martinez-Fierro, M.L. Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis. Diagnostics 2017, 7, 9. https://doi.org/10.3390/diagnostics7010009

AMA Style

Galván-Tejada CE, Zanella-Calzada LA, Galván-Tejada JI, Celaya-Padilla JM, Gamboa-Rosales H, Garza-Veloz I, Martinez-Fierro ML. Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis. Diagnostics. 2017; 7(1):9. https://doi.org/10.3390/diagnostics7010009

Chicago/Turabian Style

Galván-Tejada, Carlos E., Laura A. Zanella-Calzada, Jorge I. Galván-Tejada, José M. Celaya-Padilla, Hamurabi Gamboa-Rosales, Idalia Garza-Veloz, and Margarita L. Martinez-Fierro. 2017. "Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis" Diagnostics 7, no. 1: 9. https://doi.org/10.3390/diagnostics7010009

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