A CNN-Based Strategy to Classify MRI-Based Brain Tumors Using Deep Convolutional Network
Source abstract
Abstract
Brain tumor is a severe health condition that kills many lives every year, and several of those casualties are from rural areas. However, the technology to diagnose brain tumors at an early stage is not as efficient as expected. Therefore, we sought to create a reliable system that can help medical professionals to identify brain tumors. Although several studies are being conducted on this issue, we attempted to establish a much more efficient and error-free classification method, which is trained with a comparatively substantial number of real datasets rather than augmented data. Using a modified VGG-16 (Visual Geometry Group) architecture on 10,153 MRI (Magnetic Resonance Imaging) images with 3 different classes (Glioma, Meningioma, and Pituitary), the network performs significantly well. It achieved a precision of 99.4% for Glioma, 96.7% for Meningioma, and 100% for Pituitary, with an overall accuracy of 99.5%. It also attained better results than several other existing CNN architectures and state-of-the-art work.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · Advanced Neural Network Applications · Machine Learning and ELM
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
Department of Artificial Intelligence, BJIT Limited, Dhaka 1212, Bangladesh
School of Computing, Engineering and Intelligent Systems, Ulster University, Northland Road, Londonderry BT48 7JL, UK
References (56)
Show source-provided references
1. Brain Tumors10.1056/NEJM200101113440207 ↗
2. Imaging of low- and intermediate-grade gliomas10.1053/srao.2001.21420 ↗
3. Day, M.R., Lacarra, E.D., Sacks, J.M., and Sadraei, A. (2022, July 13). Theranostics of Glioblastoma Multiforme: In Vitro Characterization of Targeted Nanoemulsions and Creation of a 3D Statistical Heatmap to Visualize Nanoemulsion Uptake. Available online: https://web.wpi.edu/Pubs/E-project/Available/E-project-043015-050001/.
4. Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods10.1016/j.procs.2016.09.407 ↗
5. Drevelegas, A. (2011). Imaging of Brain Tumors with Histological Correlations, Springer.10.1007/978-3-540-87650-2 ↗
6. A survey on deep learning in medical image analysis10.1016/j.media.2017.07.005 ↗
7. Automated segmentation of MR images of brain tumors10.1148/radiology.218.2.r01fe44586 ↗
8. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary10.1007/s00401-016-1545-1 ↗
9. MRI based medical image analysis: Survey on brain tumor grade classification10.1016/j.bspc.2017.07.007 ↗
10. Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., and Feng, Q. (2015). Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE, 10.10.1371/journal.pone.0140381 ↗
11. Cheng, J., Yang, W., Huang, M., Huang, W., Jiang, J., Zhou, Y., Yang, R., Zhao, J., Feng, Y., and Feng, Q. (2016). Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation. PLoS ONE, 11.10.1371/journal.pone.0157112 ↗
12. Brain tumor classification for MR images using transfer learning and fine-tuning10.1016/j.compmedimag.2019.05.001 ↗
13. Rajesh, T., and Malar, R.S.M. (2013, January 24–26). Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images. Proceedings of the International Conference on “Advanced Nanomaterials and Emerging Engineering Technologies”, ICANMEET 2013, Chennai, India.10.1109/ICANMEET.2013.6609287 ↗
14. Machhale, K., Nandpuru, H.B., Kapur, V., and Kosta, L. (2015, January 28–30). MRI brain cancer classification using hybrid classifier (SVM-KNN). Proceedings of the 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015, Pune, India.10.1109/IIC.2015.7150592 ↗
15. Shasidhar, M., Raja, V.S., and Kumar, B.V. (2011, January 3–5). MRI brain image segmentation using modified fuzzy c-means clustering algorithm. Proceedings of the 2011 International Conference on Communication Systems and Network Technologies, CSNT 2011, Katra, India.10.1109/CSNT.2011.102 ↗
16. Goswami, M.S., and Bhaiya, M.L.K.P. (2013, January 6–8). Brain tumour detection using unsupervised learning based neural network. Proceedings of the 2013 International Conference on Communication Systems and Network Technologies, CSNT 2013, Gwalior, India.10.1109/CSNT.2013.123 ↗
17. Brain tumor detection and multi-classification using advanced deep learning techniques10.1002/jemt.23688 ↗
18. A decision support system for multimodal brain tumor classification using deep learning10.1007/s40747-021-00321-0 ↗
19. Classifcation of brain tumours in MR images using deep spatiospatial models10.1038/s41598-022-05572-6 ↗
20. Radiological tumour classification across imaging modality and histology10.1038/s42256-021-00377-0 ↗
21. Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer10.1038/s41467-020-18703-2 ↗
22. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning10.1038/s41591-018-0177-5 ↗
23. Introduction to Convolutional Neural Networks
24. Deep learning10.1038/nature14539 ↗
25. Nair, V., and Hinton, G.E. (, January 21–June). Rectified linear units improve restricted boltzmann machines. Procedings of the 27th International Conference on Machine Learning, ICML 2010, Haifa, Israel.
26. FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
27. Xu, B., Wang, N., Chen, T., and Li, M. (2022, June 28). Empirical Evaluation of Rectified Activations in Convolutional Network. Available online: http://arxiv.org/abs/1505.00853.
28. Ramachandran, P., Zoph, B., and Le Google Brain, Q. (May, January 30). Swish: A Self-Gated Activation Function. Proceedings of the 6th International Conference on Learning Representations, ICLR 2018—Workshop Track Proceedings, Vancouver, BC, Canada.
29. Ma, N., Zhang, X., Liu, M., and Sun, J. (2020, January 10). Activate or Not: Learning Customized Activation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.10.1109/CVPR46437.2021.00794 ↗
30. Bridle, J.S. (1989, January 27–30). Training Stochastic Model Recognition Algorithms Training Stochastic Model Recognition Algorithms as Networks can lead to Maximum Mutual Information Estimation of Parameters. Proceedings of the 2nd International Conference on Neural Information Processing Systems, Denver, CO, USA.
31. Pan, Y., Huang, W., Lin, Z., Zhu, W., Zhou, J., Wong, J., and Ding, Z. (2015, January 25–29). Brain tumor grading based on Neural Networks and Convolutional Neural Networks. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Milan, Italy.10.1109/EMBC.2015.7318458 ↗
32. Othman, M.F., and Basri, M.A.M. (2011, January 25–27). Probabilistic Neural Network for brain tumor classification. Proceedings of the 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, Phnom Penh, Cambodia.10.1109/ISMS.2011.32 ↗
33. Multi-Classification of Brain Tumor Images Using Deep Neural Network10.1109/ACCESS.2019.2919122 ↗
34. Deep learning based brain tumor classification and detection system10.3906/elk-1801-8 ↗
35. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images10.1016/j.bspc.2019.101678 ↗
36. Brain Tumor Detection and Classification Using Deep Learning Classifier on MRI Images
37. Brain Tumor Classification Using Convolutional Neural Networks10.13005/bpj/1511 ↗
38. Multi-grade brain tumor classification using deep CNN with extensive data augmentation10.1016/j.jocs.2018.12.003 ↗
39. Classification using deep learning neural networks for brain tumors10.1016/j.fcij.2017.12.001 ↗
40. Khan, M.A., Ashraf, I., Alhaisoni, M., Damaševičius, R., Scherer, R., Rehman, A., and Bukhari, S.A.C. (2020). Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics, 10.10.3390/diagnostics10080565 ↗
41. Brain tumor classification using deep CNN features via transfer learning10.1016/j.compbiomed.2019.103345 ↗
42. MCNN: A multi-level CNN model for the classification of brain tumors in IoT-healthcare system
43. Raza, A., Ayub, H., Khan, J.A., Ahmad, I., SSalama, A., Daradkeh, Y.I., Javeed, D., Ur Rehman, A., and Hamam, H. (2022). A Hybrid Deep Learning-Based Approach for Brain Tumor Classification. Electronics, 11.10.3390/electronics11071146 ↗
44. El Kader, I.A., Xu, G., Shuai, Z., Saminu, S., Javaid, I., and Ahmad, I.S. (2021). Differential Deep Convolutional Neural Network Model for Brain Tumor Classification. Brain Sci., 11.10.3390/brainsci11030352 ↗
45. Alanazi, M.F., Ali, M.U., Hussain, S.J., Zafar, A., Mohatram, M., Irfan, M., AlRuwaili, R., Alruwaili, M., Ali, N.H., and Albarrak, A.M. (2022). Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model. Sensors, 22.10.3390/s22010372 ↗
46. (2022, October 05). Brain Tumor Dataset | Kaggle. Available online: https://www.kaggle.com/datasets/nniisshhaann/braintumors.
47. (2022, October 05). Brain Tumor Dataset. Available online: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427?fbclid=IwAR3VaHmuktRYQKmRLMYpDVD2DUxFWQqMlm4fqe5voJuNidukZD6WzC-kf0U.
48. Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv, Available online: http://arxiv.org/abs/1409.1556.
49. Keskar, N.S., and Socher, R. (2017). Improving Generalization Performance by Switching from Adam to SGD. arXiv, Available online: http://arxiv.org/abs/1712.07628.
50. Thakkar, V., Tewary, S., and Chakraborty, C. (2018, January 12–13). Batch Normalization in Convolutional Neural Networks—A comparative study with CIFAR-10 data. Proceedings of the 5th International Conference on Emerging Applications of Information Technology, EAIT 2018, Kolkata, India.10.1109/EAIT.2018.8470438 ↗
51. A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data10.1016/j.eswa.2022.118045 ↗
52. Paul, J.S., Plassard, A.J., Landman, B.A., and Fabbri, D. (2017). Deep learning for brain tumor classification. Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, SPIE.10.1117/12.2254195 ↗
53. Afshar, P., Plataniotis, K.N., and Mohammadi, A. (2019, January 12–17). Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries. Proceedings of the ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8683759 ↗
54. Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms10.1016/j.bbe.2018.10.004 ↗
55. Tan, M., and Le, Q.V. (2019, January 9–15). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, PMLR, Long Beach, CA, USA.
56. He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7–12). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2016.90 ↗