Home > Archive > 2020 > Volume 10 Number 2 (Feb. 2020) >
IJMLC 2020 Vol.10(2): 400-405 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.949

Brain Tumor Detection Using Skull Stripping and U-Net Architecture

Debapriya Hazra and Yungcheol Byun

Abstract—The formation of abnormal cells within the brain is termed as brain tumor. Brain tumors can be benign or cancerous. Brain being the major organ of the body controls every activity we perform in our daily life. Therefore, it is important to detect any kind of abnormal growth of cells in the brain at the earliest. Deep learning methods are now being hugely used in medical image analysis. Manual detection of brain tumors by radiologists are time consuming and error prone. Therefore applying Machine Learning techniques to automatically segment tumor region and to detect is very important and necessary in advancing medical image analysis. The algorithm in this paper could automatically detect brain tumor through skull stripping method and segmentation through U-Net architecture. The algorithm has been tested on 3000 Magnetic resonance imaging images (MRI) and resulted in an accuracy of 93%. Dataset of Digital Imaging and Communications in Medicine (DICOM) format brain MRI images has been used for the experimentation. The proposed method achieved mean Dice Similarity Coefficient metric of 0.82 and median Dice Similarity Coefficient metric of 0.86 for full tumor region.

Index Terms—Skull stripping, U-Net architecture, data augmentation.

Debapriya Hazra and Yungcheol Byun are with the Jeju National University, South Korea (corresponding author: Yungcheol Byun; e-mail: debapriyah@gmail.com, yungcheolbyun@gmail.com).


Cite: Debapriya Hazra and Yungcheol Byun, "Brain Tumor Detection Using Skull Stripping and U-Net Architecture," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 400-405, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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