International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




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Masters Thesis | Electronics & Communication Engineering | India | Volume 11 Issue 1, January 2022


An Automated Detection and Segmentation of Tumor in Brain MRI using Machine Learning Technique

Priyanka Bharti


Abstract: Magnetic Resonance Imaging can be helpful in detecting brain tumor. It has advantages over procedures like Computed Tomography scan as it does not involve use of ionizing radiation due to which exposure of a person to such risks and related side effects can be prevented. Whatever be the technique of the imaging process, the one with the maximum accuracy must be preferred. Detection of brain tumor occurs in various stages like pre-processing, segmentation, feature extraction and classifier. For this to happen, k-mean segmentation approach is applied. Gray Level Co-Occurrence Matrix and Discrete Wavelet Transform are helpful in extraction of the tumor feature. Two types of classifiers are used for classification namely Support Vector Machine and Hidden Markov Model. Then the comparison is done based on performance parameters like sensitivity, specificity and accuracy. After calculating the results, the values of performance parameters are compared. The proposed technique has been found to perform well in terms of accuracy as compared to previous technique.


Keywords: Brain tumor, Gray Level Co-Occurrence Matrix, Hidden Markov Model, K-Mean, Magnetic Resonance Image, Principal Component Analysis, Support Vector Machine


Edition: Volume 11 Issue 1, January 2022,


Pages: 51 - 62

An Automated Detection and Segmentation of Tumor in Brain MRI using Machine Learning Technique


How to Cite this Article?

Priyanka Bharti, "An Automated Detection and Segmentation of Tumor in Brain MRI using Machine Learning Technique", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=MR211230231126, Volume 11 Issue 1, January 2022, 51 - 62, #ijsrnet

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