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Reseach Article

Brain Tumor Detection and Segmentation using Deep Learning

by Mugdha Deokar, Varun Godse
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 38
Year of Publication: 2021
Authors: Mugdha Deokar, Varun Godse
10.5120/ijca2021921783

Mugdha Deokar, Varun Godse . Brain Tumor Detection and Segmentation using Deep Learning. International Journal of Computer Applications. 183, 38 ( Nov 2021), 33-38. DOI=10.5120/ijca2021921783

@article{ 10.5120/ijca2021921783,
author = { Mugdha Deokar, Varun Godse },
title = { Brain Tumor Detection and Segmentation using Deep Learning },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2021 },
volume = { 183 },
number = { 38 },
month = { Nov },
year = { 2021 },
issn = { 0975-8887 },
pages = { 33-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number38/32181-2021921783/ },
doi = { 10.5120/ijca2021921783 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:02.213459+05:30
%A Mugdha Deokar
%A Varun Godse
%T Brain Tumor Detection and Segmentation using Deep Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 38
%P 33-38
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain tumors are highly fatal and diagnosis can often be time-consuming. Timely attention and proper consultation is required to successfully detect the tumor. However, the detection of brain tumors is a very tedious task. Brain tumors are of numerous types which pose a challenging task of detection and classification as the tumors can be ill-defined with soft tissues. Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) scans, and Ultrasound images are generally used to get the brain images. Brain tumors are common, and because of the associated risk of malignancy and hyperfunction, these tumors have to be examined thoroughly. To automate the process of detection and further segmentation, a robust system is required which can be used to produce accurate results. In this work, this observation is taken into consideration and a technique was proposed that will bridge the gap between diagnosing and detecting tumors. This process will further provide a valuable second opinion to medical professionals. The methodology involves brain tumor detection using transfer learning using Residual Networks (ResNet). Further, segmentation is done to identify the area of the tumor in the MRI scan. This work demonstrates Deep Learning’s potential in processing and extracting information from MRI images to provide a non-invasive tool for automated tumor detection and segmentation for clinical applications.

References
  1. Kavitha AngamuthuRajasekaran and ChellamuthuChinnaGounder (March 14th 2018). Advanced Brain Tumour Segmentation from MRI Images, High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications, Ahmet MesrurHalefoğlu, IntechOpen, DOI: 10.5772/intechopen.71416. Available from: https://www.intechopen.com/chapters/58837
  2. Brain Tumor: Statistics, Cancer Net (https://www.cancer.net/cancer-types/brain-tumor/statistics), (Accessed 14 Aug 2021)
  3. S. Bauer, R. Wiest, L. P. Nolte, and M. Reyes, “A survey of MRI-based medical image analysis for brain tumour studies,” 2013, [Online].
  4. Dietmar Krex, Barbara Klink, Christian Hartmann, Andreas von Deimling, Torsten Pietsch, Matthias Simon, Michael Sabel, Joachim P. Steinbach, Oliver Heese, Guido Reifenberger, Michael Weller, Gabriele Schackert, for the German Glioma Network, Long-term survival with glioblastoma multiforme, Brain, Volume 130, Issue 10, October 2007, Pages 2596–2606, https://doi.org/10.1093/brain/awm204
  5. T. Hossain, F. S. Shishir, M. Ashraf, M. A. Al Nasim and F. Muhammad Shah, "Brain Tumor Detection Using Convolutional Neural Network," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019
  6. Seetha, J & Selvakumar Raja, S. (2018). “Brain Tumor Classification Using Convolutional Neural Networks. Biomedical and Pharmacology Journal”. 11. 1457-1461. 10.13005/bpj/1511.
  7. S. Grampurohit, V. Shalavadi, V. R. Dhotargavi, M. Kudari and S. Jolad, "Brain Tumor Detection Using Deep Learning Models," 2020 IEEE India Council International Subsections Conference (INDISCON), 2020, pp. 129-134, doi: 10.1109/INDISCON50162.2020.00037.
  8. Jha, Debesh & Smedsrud, Pia & Riegler, Michael & Johansen, Dag & de Lange, Thomas & Halvorsen, Pål& Johansen, Håvard&Simulamet,. (2019). ResUNet++: An Advanced Architecture for Medical Image Segmentation.
  9. M. Ali, S. O. Gilani, A. Waris, K. Zafar and M. Jamil, "Brain Tumour Image Segmentation Using Deep Networks," in IEEE Access, vol. 8, pp. 153589-153598, 2020, doi: 10.1109/ACCESS.2020.3018160.
  10. Pereira S, Pinto A, Alves V, Silva CA. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Trans Med Imaging. 2016 May;35(5):1240-1251. doi: 10.1109/TMI.2016.2538465. Epub 2016 Mar 4. PMID: 26960222.
  11. L. G. Nyul, J. K. Udupa, and X. Zhang, “New variants of a method of mri scale standardization,” IEEE Transactions on Medical Imaging, vol. 19, no. 2, pp. 143–150, 2000.
  12. [Dataset] Brain Image Segmentation; Mateusz Buda, AshirbaniSaha, Maciej A. Mazurowski "Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm." Computers in Biology and Medicine, 2019; https://www.kaggle.com/mateuszbuda/lgg-mri-segmentation
Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning Brain Tumor Segmentation Brain Tumor Detection MRI Neural Networks.