[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Browse::
Journal Info::
Guide for Authors::
Submit Manuscript::
Articles archive::
For Reviewers::
Contact us::
Site Facilities::
Reviewers::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 14, Issue 1 (9-2024) ::
JGST 2024, 14(1): 25-36 Back to browse issues page
Using deep learning-based classification methods for interpreting brain MRI images for tumor diagnosis
Fateme Bagheri , Asghar Milan *
Abstract:   (503 Views)
The classification of brain tumors is very important for evaluating and diagnosing the type of tumors and making decisions for treatment according to the stages of disease progression. Many imaging techniques are used to diagnose brain tumors. However, the MRI method is superior compared to other methods due to better image quality and not relying on ionizing radiation. It is obvious that the more accurate the interpretation is, the more it will help the treatment process, and for this purpose, image classification methods that are widely used in remote sensing can be used. Deep learning is a sub-branch of machine learning, and in recent years, it has had a remarkable performance, especially in the topics of image classification and segmentation. In this article, a deep learning model based on a convolutional neural network is proposed to classify different types of brain tumor using a dataset that classifies tumors into meningioma, glioma, and pituitary. MRI imaging methods have different protocols, in this research, the images obtained based on the T1 protocol with a total of 3064 images, which include the images of 233 patients, were used. With the proposed network structure, the overall accuracy of 97.41% was obtained for the data set. The research results show the ability of the model for brain tumor classification purposes.
 
Article number: 3
Keywords: Brain Tumor, Convolutional Neural Network, Data Augmentation, Deep Learning
Full-Text [PDF 1033 kb]   (342 Downloads)    
Type of Study: Research | Subject: Photo&RS
Received: 2024/04/30
References
1. DeAngelis, L.M., Brain tumors. New England journal of medicine, 2001. 344(2): p. 114-123. [DOI:10.1056/NEJM200101113440207]
2. Behin, A., et al., Primary brain tumours in adults. The Lancet, 2003. 361(9354): p. 323-331. [DOI:10.1016/S0140-6736(03)12328-8]
3. Drevelegas, A. and N. Papanikolaou, Imaging of brain tumors with histological correlations. 2002: Springer. [DOI:10.1007/978-3-662-04951-8]
4. Litjens, G., et al., A survey on deep learning in medical image analysis. Medical image analysis, 2017. 42: p. 60-88. [DOI:10.1016/j.media.2017.07.005]
5. Goodenberger, M.L. and R.B. Jenkins, Genetics of adult glioma. Cancer genetics, 2012. 205(12): p. 613-621. [DOI:10.1016/j.cancergen.2012.10.009]
6. Louis, D.N., et al., The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta neuropathologica, 2016. 131: p. 803-820. [DOI:10.1007/s00401-016-1545-1]
7. Bishop, C., Pattern recognition and machine learning. Springer google schola, 2006. 2: p. 5-43.
8. Deng, L. and D. Yu, Deep learning: methods and applications. Foundations and trends® in signal processing, 2014. 7(3-4): p. 197-387. [DOI:10.1561/2000000039]
9. LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436-444. [DOI:10.1038/nature14539]
10. LeCun, Y., et al., Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. 86(11): p. 2278-2324. [DOI:10.1109/5.726791]
11. Krizhevsky, A., I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012. 25.
12. Zacharaki, E.I., et al., Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2009. 62(6): p. 1609-1618. [DOI:10.1002/mrm.22147]
13. El-Dahshan, E.-S.A., T. Hosny, and A.-B.M. Salem, Hybrid intelligent techniques for MRI brain images classification. Digital signal processing, 2010. 20(2): p. 433-441. [DOI:10.1016/j.dsp.2009.07.002]
14. Cheng, J., et al., Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one, 2015. 10(10): p. e0140381. [DOI:10.1371/journal.pone.0140381]
15. Ertosun, M.G. and D.L. Rubin. Automated grading of gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks. in AMIA annual symposium proceedings. 2015. American Medical Informatics Association.
16. Paul, J.S., et al. Deep learning for brain tumor classification. in Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging. 2017. SPIE. [DOI:10.1117/12.2254195]
17. Afshar, P., K.N. Plataniotis, and A. Mohammadi. Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. in ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). 2019. IEEE. [DOI:10.1109/ICASSP.2019.8683759]
18. Anaraki, A.K., M. Ayati, and F. Kazemi, Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. biocybernetics and biomedical engineering, 2019. 39(1): p. 63-74. [DOI:10.1016/j.bbe.2018.10.004]
19. Najaf-Zadeh, A. and H.R. Ghaffari, A Two-Dimensional Convolutional Neural Network for Brain Tumor Detection From MRI. Internal Medicine Today, 2020. 26(4): p. 398-413. [DOI:10.32598/hms.26.4.3303.1]
20. Cheng, J., Brain tumor dataset. figshare. Dataset, 2017. 1512427(5).
21. Wong, S.C., et al. Understanding data augmentation for classification: when to warp? in 2016 international conference on digital image computing: techniques and applications (DICTA). 2016. IEEE. [DOI:10.1109/DICTA.2016.7797091]
22. Ioffe, S. and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. in International conference on machine learning. 2015. pmlr.
23. Scherer, D., A. Müller, and S. Behnke. Evaluation of pooling operations in convolutional architectures for object recognition. in International conference on artificial neural networks. 2010. Springer. [DOI:10.1007/978-3-642-15825-4_10]
24. Nagi, J., et al. Max-pooling convolutional neural networks for vision-based hand gesture recognition. in 2011 IEEE international conference on signal and image processing applications (ICSIPA). 2011. IEEE. [DOI:10.1109/ICSIPA.2011.6144164]
25. Srivastava, N., et al., Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 2014. 15(1): p. 1929-1958.
26. Nasrabadi, N.M., Pattern recognition and machine learning. Journal of electronic imaging, 2007. 16(4): p. 049901. [DOI:10.1117/1.2819119]
27. Ciregan, D., U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. in 2012 IEEE conference on computer vision and pattern recognition. 2012. IEEE. [DOI:10.1109/CVPR.2012.6248110]
28. Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. [DOI:10.1109/CVPR.2015.7298594]
29. Goodfellow, I., Y. Bengio, and A. Courville, Deep learning. 2016: MIT press.
30. Bottou, L. Large-scale machine learning with stochastic gradient descent. in Proceedings of COMPSTAT'2010: 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers. 2010. Springer.
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Bagheri F, milan A. Using deep learning-based classification methods for interpreting brain MRI images for tumor diagnosis. JGST 2024; 14 (1) : 3
URL: http://jgst.issgeac.ir/article-1-1184-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 14, Issue 1 (9-2024) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology