Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis
AbstractBreast 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
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.Chicago/Turabian Style
Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L. 2017. "Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis." Diagnostics 7, no. 1: 9.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.