Convolution Neural Network Based Deep Learning Approach for MRI Based Brain Tumour Image Detection
DOI:
https://doi.org/10.17010/ijcs/2024/v9/i4/174566Keywords:
Brain tumor detection
, Digital Image Processing, Deep Learning, Machine learning, Structural Magnetic Resonance Imaging, Support Vector Machine, Convolution Neural Networks.Paper Submission Date
, June 23, 2024, Paper sent back for Revision, July 8, Paper Acceptance Date, July 10, Paper Published Online, August 5, 2024.Abstract
These days, searching through the vast amount of MRI (Magnetic Resonance Imaging) images by hand to find brain cancer is a very time-consuming and confusing process. It might influence how easily patients have access to the appropriate amount of care. It can be a prolonged operation because it needs a lot of image datasets. It is difficult to distinguish between the tumor regions because brain cancer cells and normal tissues have similar appearances. Consequently, the need arises for an extremely precise automatic tumor identification method.Downloads
Published
How to Cite
Issue
Section
References
H. Kasban, M. El-bendary, and D. Salama, “A comparative study of medical imaging techniques,†Int. J. Inf. Sci. Intell. Syst,. vol. 4, no. 2, pp. 37–58, Apr. 2015.
D. S. Prabha and J. S. Kumar, “Performance evaluation of image segmentation using objective methods,†Indian J. Sci. Technol,. vol. 9, no. 8, Feb. 2016, doi: 10.17485/ijst/2016/v9i8/87907.
A. Mustaqeem, A. Javed, and T. Fatima, “An efficient brain tumor detection algorithm using watershed & thresholding based segmentation,â€Int. J. Image Graph. Signal Process, vol. 4, no. 10, pp. 34–39, 2012, doi: : 10.5815/ijigsp.2012.10.05.
Brain Tumour: Statistics, Cancer.Net Editorial Board, 1/2021. (Accessed on January 2021).
N. Gordillo, E. Montseny, and P. Sobrevilla, “State of the art survey on MRI brain tumour segmentation,†Magnetic Resonance Imaging, vol. 31, no. 8, pp. 1426–1438, 2013, doi: 10.1016/j.mri.2013.05.002.
D. White, A. Houston, W. Sampson, G. Wilkins, “Intra- and interoperator variations in region-of-interest drawing and their effect on the measurement of glomerular filtration rates,†Clinical Nuclear Medicine, vol. 24, no. 3, pp. 177–181, Mar. 1999.
J. L. Foo, “A survey of user interaction and automation in medical image segmentation methods,†Tech rep ISUHCI20062, Human Computer Interaction Department, Iowa State Univ., 2006.
A. Hamamci and N. Kucuk et al., “Tumour-cut: Segmentation of brain Tumours on contrast enhanced MR images for Radiosurgery applications,†IEEE Trans. Med. Imaging, vol. 31, no.3, pp. 790–804, 2015.
M. Havaei, H. Larochelle, P. Poulin, and P. M. Jadoin, “Within-brain classification for brain tumour segmentation,†Int. J. Cars, vol. 11, pp. 777–788, 2016, doi: https://doi.org/10.1007/s11548-015-1311-1.
M. Prastawa, E. Bullitt, and G. Gerig, “Simulation of brain tumours in MR images for evaluation of segmentation efficacy,†Med. Image Anal., vol. 13, no. 2, pp. 297–311, 2009, doi: https://doi.org/10.1016/j.media.2008.11.002.
Y. Wang and Y. Zhang et al., “Modality-pairing learning for brain tumour segmentation,†arXiv:2010.09277v2 [eess.IV], 29 Dec. 2020, doi: 10.48550/arXiv.2010.09277.
W. Zhang, Y. Wu, B. Yang, S. Hu, L. Wu, and S. Dhelim, “Overview of multi-modal brain tumour MR image segmentation,†Healthcare, vol. 9, no. 8, 1051, 2021, doi: 10.3390/healthcare9081051.
“Brain Tumour Detection v1.0 || CNN, VGG-16,†[Online]. Available: https://www.kaggle.com/code/ruslankl/brain-tumor-detection-v1-0-cnn-vgg-16#5
