A Machine Learning Approach for MRI Brain Tumor Classification

A new method for the denoising, extraction and tumor detection on MRI images is presented in this paper. MRI images help physicians study and diagnose diseases or tumors present in the brain. This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis...

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Bibliographic Details
Published inComputers, materials & continua Vol. 53; no. 2; p. 91
Main Authors Gurusamy, Ravikumar, Subramaniam, Vijayan
Format Journal Article
LanguageEnglish
Published Henderson Tech Science Press 2017
Subjects
Brain
Classification
Feature extraction
Image acquisition
Image detection
Machine learning
Magnetic resonance imaging
Medical imaging
Noise reduction
Physicians
Preprocessing
Tumors
Wavelet transforms
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Summary:A new method for the denoising, extraction and tumor detection on MRI images is presented in this paper. MRI images help physicians study and diagnose diseases or tumors present in the brain. This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis. The ambiguity of Magnetic Resonance (MR) image features is solved in a simpler manner. The MRI image acquired from the machine is subjected to analysis in the work. The real-time data is used for the analysis. Basic preprocessing is performed using various filters for noise removal. The de-noised image is segmented, and the feature extractions are performed. Features are extracted using the wavelet transform. When compared to other methods, the wavelet transform is more suitable for MRI image feature extraction. The features are given to the classifier which uses binary tree support vectors for classification. The classification process is compared with conventional methods.
ISSN:1546-2218
1546-2226
DOI:10.3970/cmc.2017.053.091