Automated defect detection in medical imaging has become the emergent field in several medical diagnostic applications. Automated detection of tumor in Magnetic Resonance Imaging (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 for large amount of data. So, automated tumor detection methods are developed as it would save radiologist time. The MRI brain tumor detection is complicated task due to complexity and variance of tumors. In this paper, tumor is detected in brain MRI using machine learning algorithms. The proposed work is divided into three parts: preprocessing steps are applied on brain MRI images, texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then classification is done using machine learning algorithm.

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