Abstract
Early diagnosis improves cancer outcomes by giving care at the most initial possible stage and is, therefore, an important health strategy in all settings. Gliomas, meningiomas, and pituitary tumors are among the most common brain tumors in adults. This paper classifies these three types of brain tumors from patients; using a Kernel Support Vector Machine (KSVM) classifier. The images are pre-processed, and its dimensionality is reduced before entering the classifier, and the difference in accuracy produced by using or not pre-processing techniques is compared, as well as, the use of three different kernels, namely linear, quadratic, and Gaussian Radial Basis (GRB) for the classifier. The experimental results showed that the proposed approach with pre-processed MRI images by using GRB kernel achieves better performance than quadratic and linear kernels in terms of accuracy, precision, and specificity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Guachi, L.: Automatic colorectal segmentation with convolutional neural network. Comput. Aided Des. Appl. 16(5), 836–845 (2019)
Nandpuru, H.B., Salankar, S.S., Bora, V.R.: MRI brain cancer classification using support vector machine. In: 2014 IEEE Students’ Conference on Electrical. Electronics and Computer Science, Bhopal, pp. 1–6. IEEE (2014)
Zhang, Y.D.: An MR brain images classifier via principal component analysis and kernel support vector machine. Prog. Electromagn. Res. 130, 369–388 (2012)
Machhale, K., Nandpuru, H.B., Kapur, V., Kosta, L.: MRI brain cancer classification using hybrid classifier (SVM-KNN). In: 2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, pp. 60–65. IEEE (2015)
Keerthana, T.K., Xavier, S.: An intelligent system for early assessment and classification of brain tumor. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, pp. 1265–1268. IEEE (2018)
National Brain Tumor Society. https://braintumor.org/brain-tumor-information/understanding-brain-tumors/tumor-types/. Accessed 26 June 2019
Figshare. https://figshare.com/articles/brain_tumor_dataset/1512427/5. Accessed 26 June 2019
Dong, J., Suen, C.Y., Krzyzak, A.: Effective shrinkage of large multi-class linear SVM models for text categorization. In: 2008 19th International Conference on Pattern Recognition, Tampa, pp. 1–4. IEEE (2008)
Dagher, I.: Quadratic kernel-free non-linear support vector machine. J. Glob. Optim. 41(1), 15–30 (2008)
Abd-Ellah, M.K., Awad, A.I., Khalaf, A.A.M., Hamed, H.F.A.: Classification of brain tumor MRIs Using a kernel support vector machine. In: Li, H., Nykänen, P., Suomi, R., Wickramasinghe, N., Widén, G., Zhan, M. (eds.) WIS 2016. CCIS, vol. 636, pp. 151–160. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44672-1_13
Github. https://github.com/RichardTorres2017/BrainTumorClassification. Accessed 16 July 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Torres-Molina, R., Bustamante-Orellana, C., Riofrío-Valdivieso, A., Quinga-Socasi, F., Guachi, R., Guachi-Guachi, L. (2019). Brain Tumor Classification Using Principal Component Analysis and Kernel Support Vector Machine. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-33617-2_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33616-5
Online ISBN: 978-3-030-33617-2
eBook Packages: Computer ScienceComputer Science (R0)