Brain Tumor Classification Using a Pre-Trained Auxiliary Classifying Style-Based Generative Adversarial Network.

M. Akshay Kumaar; Duraimurugan Samiayya; Venkatesan Rajinikanth; P. M. Durai Raj Vincent; Seifedine Kadry

doi:10.9781/ijimai.2023.02.008

Authors

M. Akshay Kumaar BrainSightAI (India).
Duraimurugan Samiayya St. Joseph’s College of Engineering
Venkatesan Rajinikanth Saveetha University
P. M. Durai Raj Vincent Vellore Institute of Technology University
Seifedine Kadry Noroff University College

DOI:

https://doi.org/10.9781/ijimai.2023.02.008

Keywords:

Generative Adversarial Network, Image, Magnetic Resonance Imaging, Medical Images, Network

Abstract

Computer Vision's applications and their use cases in the medical field have grown vastly in the past decade. The algorithms involved in these critical applications have helped doctors and surgeons perform procedures on patients more precisely with minimal side effects. However, obtaining medical data for developing large scale generalizable and intelligent algorithms is challenging in the real world as multiple socio-economic, administrative, and demographic factors impact it. Furthermore, training machine learning algorithms with a small amount of data can lead to less accuracy and performance bias, resulting in incorrect diagnosis and treatment, which can cause severe side effects or even casualties. Generative Adversarial Networks (GAN) have recently proven to be an effective data synthesis and augmentation technique for training deep learning-based image classifiers. This research proposes a novel approach that uses a Style-based Generative Adversarial Network for conditional synthesis and auxiliary classification of Brain Tumors by pre-training. The Discriminator of the pre-trained GAN is fine-tuned with extensive data augmentation techniques to improve the classification accuracy when the training data is small. The proposed method was validated with an open-source MRI dataset which consists of three types of tumors - Glioma, Meningioma, and Pituitary. The proposed system achieved 99.51% test accuracy, 99.52% precision score, and 99.50% recall score, significantly higher than other approaches. Since the framework can be made adaptive using transfer learning, this method also benefits new and small datasets of similar distributions.

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References

M. Sharif, J. Li, M. Khan, S. Kadry, and U. Tariq, “M3BTCNet: multi model brain tumor classification using metaheuristic deep neural network features optimization,” Neural Computing and Applications, vol. 36, pp. 95-110, 2018, doi: 10.1007/s00521-022-07204-6.

M. Nawaz, T. Nazir, M. Masood, A. Mehmood, R. Mahum, M.A. Khan, S. Kadry, O. Thinnukool, “Analysis of Brain MRI Images Using Improved CornerNet Approach,” Diagnostics, vol. 11, no. 10, pp. 1-18, 2022, doi: 10.3390/diagnostics11101856.

A. Aziz, M. Attique, U. Tariq, Y. Nam, M. Nazir, C. Jeong, R. R. Mostafa, R. H. Sakr, “An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification,” Computers, Materials and Continua, vol. 69, no. 2, pp. 2653-2670, 2021, doi: 10.32604/cmc.2021.018606.

M.I. Sharif, M.A. Khan, M. Alhussein, K. Aurangzeb, M. Raza, “A decision support system for multimodal brain tumor classification using deep learning,” Complex & Intelligent Systems, vol. 8, 2022, doi: 10.1007/s40747-021-00321-0.

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 (CVPR), 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90.

D. R. Yerukalareddy and E. Pavlovskiy, “Brain Tumor Classification based on MR Images using GAN as a Pre-Trained Model,” in Proceedings of the 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB), pp. 380-384, 2021, doi: 10.1109/CSGB53040.2021.9496036.

N. Ghassemi, A. Shoeibi, and M. Rouhani, “Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images,” Biomedical Signal Processing and Control, vol. 57, pp. 101678, 2020, doi: 10.1016/j.bspc.2019.101678.

S. Deepak and P. M. Ameer, “MSG-GAN Based Synthesis of Brain MRI with Meningioma for Data Augmentation,” in Proceedings of the 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1-6, 2020, doi: 10.1109/CONECCT50063.2020.9198672.

C. Oeldorf and G. Spanakis, “LoGANv2: Conditional Style-Based Logo Generation with Generative Adversarial Networks,” pp. 462-468, 2019, doi: https://arxiv.org/abs/1909.09974

T. Karras, S. Laine, and T. Aila, “A Style-Based Generator Architecture for Generative Adversarial Networks,” in Proceedings of the 2019 IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4396-4405, 2019, doi: 10.1109/CVPR.2019.00453.

J. Cheng, “Brain Tumor Dataset 2017,” 2017. [Online]. Available: https://doi.org/10.6084/m9.figshare.1512427.v5

G. Xiao et al., “Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI,” Micromachines, vol. 13, no. 1, pp. 15, 2022, doi: 10.3390/mi13010015.

F. Díaz-Pernas, M. Martínez Zarzuela, M. Antón-Rodríguez, and D. González-Ortega, “A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network,” Healthcare, vol. 9, pp. 153, 2021, doi: 10.3390/healthcare9020153.

M. Karnati, A. Seal, G. Sahu, and A. Yazidi, “A novel multiscale-based deep convolutional neural network for detecting COVID-19 from X-rays,” Applied Soft Computing, vol. 125, 2022, doi: 10.1016/j.asoc.2022.109109.

R. L. Kumar, J. Kakarla, B.V. Isunuri, M. Singh, “Multi-class brain tumor classification using residual network and global average pooling,” Multimedia Tools and Applications, vol. 80, pp. 13429–13438, 2021, doi: 10.1007/s11042-020-10335-4.

R. Singh, A. Goel, and D. K. Raghuvanshi, “Computer-aided diagnostic network for brain tumor classification employing modulated Gabor filter banks,” The Visual Computer, vol. 37, pp. 2157–2171, 2020, doi: 10.1007/s00371-020-01977-4.

I. Abd El Kader, X. Guizhi, Z. Shuai, S. Saminu, I. Javaid, and I. S. Ahmad, “Differential Deep Convolutional Neural Network Model for Brain Tumor Classification,” Brain Sciences, vol. 11, no. 3, pp. 352, 2021, doi: 10.3390/brainsci11030352.

J. Kang, Z. Ullah, and J. Gwak, “MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers,” Sensors, vol. 21, no. 6, pp. 2222, 2021, doi: 10.3390/s21062222.

M. Alshayeji, J. Al-Buloushi, A. Ashkanani, S. Abed, “Enhanced brain tumor classification using an optimized multi-layered convolutional neural network architecture,” Multimedia Tools and Applications, vol. 80, pp. 28897–28917, 2021, doi: 10.1007/s11042-021-10927-8.

M. Arbane, R. Benlamri, Y. Brik, and M. Djerioui, “Transfer Learning for Automatic Brain Tumor Classification Using MRI Images,” in Proceedings of the 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), 2021, pp. 210-214, doi: 10.1109/CSGB53040.2021.9496036.

A. B. T. Tahir, M.A. Khan, M. Alhaisoni, J. Ali Khan, Y. Nam, S. Wang, K. Javed, “Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification,” Computers, Materials and Continua, vol. 68, no. 1, pp. 1099-1116, 2021, doi: 10.32604/cmc.2021.015154.

A. Rehman, M. Khan, T. Saba, Z. Mehmood, U. Tariq, and N. Ayesha, “Microscopic Brain Tumor Detection and Classification using 3D CNN and Feature Selection Architecture,” Microscopy Research and Technique, vol. 84, pp. 133-149, 2020, doi: 10.1002/jemt.23597.

S. Deepak and P. M. Ameer, “Automated Categorization of Brain Tumor from MRI Using CNN features and SVM,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 8357–8369, 2021, doi: 10.1007/s12652-020-02568-w.

R. Singh, A. Goel, and D. K. M. R. Raghuvanshi, “Brain tumor classification employing ICA and kernel-based support vector machine,” Signal, Image and Video Processing, vol. 15, pp. 501–510, 2021, doi: 10.1007/s11760-020-01770-9.

M. M. Badža and M. Č. Barjaktarović, “Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network,” Applied Sciences, vol. 10, no. 6, p. 1999, 2020, doi: 10.3390/app10061999.

J. Y. Zhu, T. Park, P. Isola, and A. Efros, “Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2242-2251, 2017, doi: 10.1109/ICCV.2017.244.

A. Rehman, S. Naz, M. I. Razzak, F. Akram, M. A. Inran, “A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning,” Circuits, Systems, and Signal Processing, vol. 39, pp. 757–775, 2020, doi: 10.1007/s00034-019-01246-3.

A. Seal, D. Bhattacharjee, and M. Nasipuri, “Predictive and probabilistic model for cancer detection using computer tomography images,” Multimedia Tools and Applications, vol. 77, 2018, doi: 10.1007/s11042-017-4405-7.

T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and Improving the Image Quality of StyleGAN,” in Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8107-8116, 2020, doi: 10.1109/CVPR42600.2020.00813.

M. Sajjad, S. Khan, K. Muhammad, W. Wu, A. Ullah, and S. Baik, “MultiGrade Brain Tumor Classification using Deep CNN with Extensive Data Augmentation,” Journal of Computational Science, vol. 30, pp. 174-182, 2018, doi: 10.1016/j.jocs.2018.12.003.

A. Karnewar and O. Wang, “MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks,” in Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7796- 7805, 2020, doi: 10.1109/CVPR42600.2020.00782.

A. Karnewar, O. Wang, and R. S. Iyengar, “MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis,” ArXiv preprint, 2019, abs/1903.06048.

J. Seetha and S. S. Raja, “Brain tumor classification using Convolutional Neural Networks,” Biomedical and Pharmacology Journal, vol. 11, no. 3, pp. 1457-1461, 2018, doi: 10.13005/bpj/1511.

M. Balasooriya and R. D. Nawarathna, “A sophisticated convolutional neural network model for brain tumor classification,” in Proceedings of the 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), Peradeniya, pp. 1-5, 2017, doi: 10.1109/ICIINFS.2017.8300364.

P. Afshar, K. N. Plataniotis, A. Mohammadi, “Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries,” in Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, pp. 1368-1372, 2019, doi: 10.1109/ICIP.2018.8451379.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, “Generative Adversarial Networks,” Advances in Neural Information Processing Systems, vol. 27, 2014, doi: 10.1145/3422622.

T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive Growing of GANs for Improved Quality, Stability, and Variation,” 2018, doi: arxiv:1710.10196v3.

X. Huang and S. Belongie, “Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization,” International Conference on Computer Vision, pp. 1510-1519, 2017, doi: arXiv:1703.06868v2.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved Training of Wasserstein GANs,” pp. 5769-5779, 2017, doi: arXiv:1704.00028v3.

D.P. Kingma, J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference on Learning Representations, 2014.