Deep Learning Model for Magnetic Resonance Imaging Brain Tumor Recognition
Keywords:Glioma, MRI, Deep Learning, Brain Tumor classification, CNN, LGG, HGG
Human interpretation of a large quantity of Magnetic Resonance Imaging (MRI images) is a tiring task and depends on the practitioner's expertise and experience. Glioma is one of the most common and dangerous types of primary brain tumors, and its early diagnosis could be life-saving. Precise and fully automatic classification of Glioma on MRI images helps physicians diagnose and monitor patients.
In this work, we propose an automatic system to aid in diagnosing Glioma by classifying brain tumors into two categories: High-Grade Glioma (HGG) and Low-Grade Glioma (LGG). To perform this task, we trained three deep learning models (VGG-16, ResNet-50, and Inception-V3) on four brain MRI datasets (one for each MRI modality). To further improve tumor classification, non-tumorous slices were removed from the HGG class of the selected dataset and then were separately used to train the three models. Evaluations on BraTS 2019 attest that T1 presents the most discriminative features with 0.9513, 0.907, and 0.9487 for accuracy, sensitivity, and specificity, respectively. The Inception-V3 model outperforms the other models with 0.9975, 0.9894, and 1 for accuracy, sensitivity, and specificity. Experimental results demonstrate that using the Inception-V3 model with T1 modality can achieve good performances.
D. N. Louis, A. Perry, G. Reifenberger, A. Von Deimling, D. Figarella- Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 world health organization classification of tu- mors of the central nervous system: a summary,” Acta neuropathologica, vol. 131, no. 6, pp. 803–820, 2016.
M. D. Guimara˜es, J. Noschang, S. R. Teixeira, M. K. Santos, H. M. Lederman, V. Tostes, V. Kundra, A. D. Oliveira, B. Hochhegger, and E. Marchiori, “Whole-body mri in pediatric patients with cancer,” Cancer Imaging, vol. 17, no. 1, pp. 1–12, 2017.
D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual review of biomedical engineering, vol. 19, pp. 221– 248, 2017.
F. B. Gonbadi and H. Khotanlou, “Glioma brain tumors diagnosis and classification in MRI images based on convolutional neural networks,” in 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019, pp. 1–5.
L. Pei, L. Vidyaratne, W.-W. Hsu, M. M. Rahman, and K. M. Iftekharud- din, “Brain tumor classification using 3d convolutional neural network,” in International MICCAI Brain lesion Workshop. Springer, 2019, pp. 335–342.
R. R. Agravat and M. S. Raval, “Brain tumor segmentation and survival prediction,” in International MICCAI Brainlesion Workshop. Springer, 2019, pp. 338–348.
S. Alqazzaz, X. Sun, X. Yang, and L. Nokes, “Automated brain tumor segmentation on multi-modal mr image using segnet,” Computational Visual Media, vol. 5, no. 2, pp. 209–219, 2019.
H. Mzoughi, I. Njeh, A. Wali, M. B. Slima, A. Ben Hamida, C. Mhiri, and K. B. Mahfoudhe, “Deep multi-scale 3d convolutional neural network (CNN) for MRI gliomas brain tumor classification,” Journal of Digital Imaging, 2020.
V. Kumar and M. L., “Deep learning as a frontier of machine learning: A review,” International Journal of Computer Applications, vol. 182, pp. 22–30, 07 2018.
A. Muniasamy and A. Alasiry, “Deep learning: The impact on future elearning,” International Journal of Emerging Technologies in Learning (iJET), vol. 15, p. 188, 01 2020.
J. Ker, L. Wang, J. Rao, and T. Lim, “Deep learning applications in medical image analysis,” Ieee Access, vol. 6, pp. 9375–9389, 2017.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
X.-F. Xu, L. Zhang, C.-D. Duan, and Y. Lu, “Research on inception module incorporated siamese convolutional neural networks to realize face recognition,” IEEE Access, vol. 8, pp. 12 168–12 178, 2019.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9.
P. Spyridon Bakas, “Multimodal brain tumor seg- mentation challenge 2020: Data.” [Online]. Available: https://www.med.upenn.edu/cbica/brats2020/data.html
How to Cite
Copyright (c) 2021 WAS Science Nature (WASSN) ISSN: 2766-7715
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.