• Priyanka Prakash Ghadge

Abstract: A tumor is a mass of tissue formed by accumulation of malignant cells. The World Health Organization (WHO) defines four grades of tumor. Grade I and II are low grade (LG) tumor while grade III and IV are high grade (HG). This paper presents a system based on a discriminative model used in multimodal MRI tumor segmentation. Early detection of diseases is of the most importance to maintaining or somehow regaining one’s health, and thus it contributes to improving quality of life. The combination of various image processing techniques creates an efficient diagnostic tool. One part of the imaging techniques is built around automatic image segmentation, which is much faster than time-consuming analysis by experts. In any case, the earlier the tumor is detected, the better the chances of survival life. In addition to sensitive automatic detection, precise segmentation of tumors is also required for efficient treatment and intervention planning. In particular, brain tumor segmentation consists of separating the different tumor tissues from normal brain tissue. Accurate and reproducible segmentation and characterization of abnormalities can be considered indispensable in medical diagnosis. The main goal of the most efficient treatment of brain tumors is early discovery, identification and diagnosis. Detection of tumor in Magnetic Resonance Imaging (MRI) is very imported which detects tumor automatically. It gives information about abnormal tissues which is helpful for planning treatment. In this paper brain tumor detection done with the help of random forest algorithm (RF)

"BRAIN TUMOR SEGMENTATION USING RANDOM FOREST ALGORITHM ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.404-409, June 2019, Available :http://www.jetir.org/papers/JETIR1907D60.pdf

"BRAIN TUMOR SEGMENTATION USING RANDOM FOREST ALGORITHM ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp404-409, June 2019, Available at : http://www.jetir.org/papers/JETIR1907D60.pdf