• Bole Swapna Trimurti
• Vaijanath V. Yerigeri.

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 [2]. Brain cancer is one of the most wide disease among peoples in the India and the world. Correct and early diagnosis is an extremely important steps in treatment. However, it is not an easy one due to several uncertainties in detection. Machine Learning (ML) techniques can be used to develop tools for physicians that can be used as an effective mechanism for early detection and diagnosis of brain cancer which will greatly enhance the survival rate of patients. In the proposed work, Machine learning technique is used for brain cancer detection and diagnosis. For analysis purpose brain cancer dataset taken from the Open Access Series of Imaging Studies (OASIS), this set contains total 373 images. Brain cancer dataset used as a training set to evaluate and compare the performance of the ML classifiers in terms of key parameters such as accuracy, recall, precision and area of ROC (Receiver Operating Characteristics). Result of Proposed work achieved by two classifiers Decision Tree and Random Forest. result obtained by these two classifiers in the form of confusion matrix and receiver operating characteristic (ROC) curve as well as the comparative study of these two classifiers.

Machine Learning (ML), Decision Tree, Random Forest.

"Brain Tumor Classification using Decision Tree and Random Forest", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.57-63, June 2019, Available :http://www.jetir.org/papers/JETIR1906O10.pdf

"Brain Tumor Classification using Decision Tree and Random Forest", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp57-63, June 2019, Available at : http://www.jetir.org/papers/JETIR1906O10.pdf