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A Learning Based Brain Tumor Detection System

Sultan Noman Qasem1,2, Amar Nazar3, Attia Qamar4, Shahaboddin Shamshirband5,6,*, Ahmad Karim4

Computer Science Department, College of Computer and information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz, Yemen.
Department of Computer Science, Bahauddin Zakariya University, Pakistan.
Department of Information Technology, Bahauddin Zakariya University, Pakistan.
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

* Corresponding Author: Shahaboddin Shamshirband. Email: .

Computers, Materials & Continua 2019, 59(3), 713-727.


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.


Cite This Article

S. Noman Qasem, A. Nazar, A. Qamar, S. Shamshirband and A. Karim, "A learning based brain tumor detection system," Computers, Materials & Continua, vol. 59, no.3, pp. 713–727, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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