BRAIN NEOPLASM CLASSIFICATION & DETECTION OF ACCURACY ON MRI IMAGES

BRAIN NEOPLASM CLASSIFICATION & DETECTION OF ACCURACY ON MRI IMAGES

GOWTHAMAN R , JAYAVIGNESH K , JEEVANANTHAM V , K.S.VEERADANYA

Abstract— The abnormal uncontrolled cell growth in the brain, commonly known n as a brain tumor, can lead to immense pressure on the various nerves and blood vessels causing irreversible harm to the body. Early detection of brain tumor is the key to avoid such compilations. Tumor detection can be done through various advanced Machine Learning and Image Processing algorithm. The various stages of brain tumor detection are image pre-processing, segmentation and feature extraction. Preprocessing includes enhancing the image by using various fitters and removing noise. Segmentation includes methods like thresholding, region growing etc. Features like lucunar infarct, meningioma, and vestibular schwannoma are calculated for the extruded tumor. Different classifiers like Artificial Neural Network, Naive Bayer are used to classify the tumor as benign or malignant.

Keywords—Component, Detection, Diagnosis, , MRI