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Efficiency and Scalability Methods in Cancer Detection Problems

Efficiency and Scalability Methods in Cancer Detection Problems

Inna Stainvas, Alexandra Manevitch
Copyright: © 2013 |Pages: 20
ISBN13: 9781466639423|ISBN10: 1466639423|EISBN13: 9781466639430
DOI: 10.4018/978-1-4666-3942-3.ch004
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MLA

Stainvas, Inna, and Alexandra Manevitch. "Efficiency and Scalability Methods in Cancer Detection Problems." Efficiency and Scalability Methods for Computational Intellect, edited by Boris Igelnik and Jacek M. Zurada, IGI Global, 2013, pp. 75-94. https://doi.org/10.4018/978-1-4666-3942-3.ch004

APA

Stainvas, I. & Manevitch, A. (2013). Efficiency and Scalability Methods in Cancer Detection Problems. In B. Igelnik & J. Zurada (Eds.), Efficiency and Scalability Methods for Computational Intellect (pp. 75-94). IGI Global. https://doi.org/10.4018/978-1-4666-3942-3.ch004

Chicago

Stainvas, Inna, and Alexandra Manevitch. "Efficiency and Scalability Methods in Cancer Detection Problems." In Efficiency and Scalability Methods for Computational Intellect, edited by Boris Igelnik and Jacek M. Zurada, 75-94. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3942-3.ch004

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Abstract

Computer aided detection (CAD) system for cancer detection from X-ray images is highly requested by radiologists. For CAD systems to be successful, a large amount of data has to be collected. This poses new challenges for developing learning algorithms that are efficient and scalable to large dataset sizes. One way to achieve this efficiency is by using good feature selection.

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