NDLI: Support vector machine (SVM) classifier for brain tumor detection

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Support vector machine (SVM) classifier for brain tumor detection

Support vector machine (SVM) classifier for brain tumor detection

Content Provider	ACM Digital Library
Author	Shubhangi, D. C. Hiremath, P. S.
Abstract	One of the primary diagnostic and treatment evaluation tools for brain tumors has been magnetic resonance (MR) imaging. MR imaging has become a widely-used method of high quality medical imaging, especially in brain imaging where MR's soft tissue contrast and non-invasiveness are clear advantages. MR images can also be used to track the size of a brain tumor as it responds (or doesn't) to treatment. A reliable method for segmenting tumor would clearly be a useful tool. Currently, however, there is no method widely accepted in clinical practice for quantitating tumor volumes from MR images. Tese manual measurements, have shown poor reproducibility and tumor response criteria based on these manual estimations have shown poor correlation with quantitative 2D and 3D metrics. Computer-based brain tumor segmentation has remained largely experimental work, with approaches including multi-spectral analysis, edge detection, neural networks, and knowledge-based techniques. We propose a real time design for brain tumor detection that a combination of knowledge-based techniques and multi-spectral analysis based on Support Vector Machines. The results shows that the system could effectively detect pathology and label normal transaxial slices.
Starting Page	444
Ending Page	448
Page Count	5
File Format	PDF
ISBN	9781605583518
DOI	10.1145/1523103.1523191
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-01-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Brain tumor Svm (support vector machine) Hyperplane
Content Type	Text
Resource Type	Article

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