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A Review of various Machine Learning Techniques for Brain Tumor Detection from MRI images

Author(s):

Aaishwarya SanjayBajaj * and Usha Chouhan  

Abstract:


Background: This paper endeavor to identify an expedient approach for detection of the Brain tumor in MRI images. The detection of tumor based on i) Review of Machine learning approach for identification of brain tumor ii) Review of a suitable approach for brain tumor detection.

Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach which better accuracy for tumor detection. This further includes the image processing method. In most application machine learning shows better performance than manual segmentation of the brain tumors from MRI images is a difficult and time-consuming task. For fast and better computational results radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research.

Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

Keywords:

Brain tumor, data mining techniques, filtering techniques, MRI, classifiers, feature selection.

Affiliation:

Department of Mathematics (Computational and System Biology), Maulana Azad National Institute of Technology, Bhopal, Department of Mathematics (Computational and System Biology), Maulana Azad National Institute of Technology, Bhopal



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