• Dr. D. R. Dhotre
• Miss. Karuna Vijay Pawar

Brain tumor is an abnormal growth of brain cells within the brain. Brain tumor detection and segmentation and is one of the most challenging and time consuming task in medical image processing. MRI (Magnetic Resonance Imaging) is a visualization medical technique, which provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumor. MRI is an imperative technique used for brain tumor detection and verdict. Study of medical MRI images by the radiologist is very difficult and time overwhelming task and correctness depending upon their experience. To overcome this problem, the automatic computer aided system becomes very obligatory. The brain tumors are classified into malignant and benign using SVM and KNN classifiers. The odds of survival can be expanded in the event that the tumor is identified effectively at its initial stage. In this paper highlight study of different techniques on brain cancer classification. In Proposed system we will use computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network (ANN) in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement, morphological operations and feature extraction have been developed for detection of the brain tumor in the MRI images of the cancer affected patients.

Classification, MRI, SVM, KNN, PCA, Skull masking, ANN.

"A SURVEY ON MRI BRAIN CANCER CLASSIFICATION TECHNIQUE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.69-75, June-2019, Available :http://www.jetir.org/papers/JETIR1906300.pdf

"A SURVEY ON MRI BRAIN CANCER CLASSIFICATION TECHNIQUE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp69-75, June-2019, Available at : http://www.jetir.org/papers/JETIR1906300.pdf