Machine Learning Algorithms for MR Brian Image Classification
S. Sreedhar Babu1, Polaiah Bojja2
1S. Sreedhar Babu, Research scholar, Professor in Department of Electronics and Communication Engineering, Koneru Lakshmaiah Educational Foundation, Greenfields, Vaddeswaram, Guntur, AP, India.
2Polaiah Bojja, Professor in Department of Electronics and Communication Engineering, Koneru Lakshmaiah Educational Foundation, Greenfields, Vaddeswaram, Guntur, AP, India.
Manuscript received on 04 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 6744-6747 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6900098319/2019©BEIESP | DOI: 10.35940/ijrte.C6900.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classification of MR brain images accurately is the crucial part in the medical imaging research since brain is a complex structure which needs multiple convergence approaches to classify. In this paper machine learning algorithms like SVM, HMM were employed to classify the brain images with the use of spatial features. Super pixel image segmentation approach is applied to segment the image into distinct super pixel region for which the features are calculated.
Keywords: Brain Classification, SVM, HMM.
Scope of the Article: Classification