A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms

Noor Salah Hassan and Nawzat Ahmed
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Noor Salah Hassan: Department of Information Technology, Akre Technical Collage, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq

International Journal of Science and Business, 2021, vol. 5, issue 6, 21-32

Abstract: The detection of the tumor region in medical images such as (MRI, CT, X-Ray) a boring and time-consuming task is by radiologists or experts. So, in this review the accuracy is needed for detection the tumor. The area of medical imaging is also reducing complexity and improving diagnostic precision with the growth of information technology. This review paper makes a comparison between the k-mean, and fuzzy c-mean algorithms to display the results and accuracy of them to detection the brain tumor. The execution of the k-mean algorithm is based on centroid, size, split process, threshold, epoch, characteristics, and number of iterations, while Fuzzy C-mean is executed on the basis of the fuzziness value and the termination condition in medical images. In comparing the efficiency parameters with the state-of-the-art processes, the experimental outcomes demonstrate the importance of medical images (MRI, CT and X-Ray) and the accuracy of each algorithm that have been discussed.

Keywords: Brain Tumor; Medical Images; Pre-processing; Segmentation; Feature Extraction; K- means (search for similar items in EconPapers)
Date: 2021
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