TY  - EJOU
AU  - Qasem, Sultan Noman  
AU  - Nazar, Amar  
AU  - Qamar, Attia  
AU  - Shamshirband, Shahaboddin   
AU  - Karim, Ahmad  

TI  - A Learning Based Brain Tumor Detection System
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 59
IS  - 3
SN  - 1546-2226

AB  - Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell partition. In this paper, we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain tumor. We considered watershed segmentation technique for brain tumor detection. The proposed methodology is divided as follows: pre-processing, computing foreground applying watershed, extract and supply features to machine learning algorithms. Consequently, this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain tumor.
KW  - Magnetic resonance imaging
KW  -  brain tumor
KW  -  watershed
KW  -  segmentation
KW  -  K-NN classification

DO  - 10.32604/cmc.2019.05617