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ABSTRACT
Automated defect detection in medical imaging has become the emergent
field in several medical diagnostic applications. Automated detection of tumor
in MRI is very crucial as it provides information about abnormal tissues which
is necessary for planning treatment. The conventional method for defect
detection in magnetic resonance brain images is human inspection. This
method is impractical due to large amount of data. Hence, trusted and
automatic classification schemes are essential to prevent the death rate of
human. So, automated tumor detection methods are developed as it would
save radiologist time and obtain a tested accuracy. The MRI brain tumor
detection is complicated task due to complexity and variance of tumors. In
this project, we propose the machine learning algorithms to overcome the
drawbacks of traditional classifiers where tumor is detected in brain MRI
using machine learning algorithms. Machine learning and image classifier
can be used to efficiently detect cancer cells in brain through MRI.
