Brain Tumor Classification Using Vanilla Convolutional Neural Networks
DOI:
https://doi.org/10.31436/ijpcc.v10i2.455Keywords:
Brain tumour, CNN, Classification, Deep learning, MRI, Vanilla-CNN.Abstract
Brain tumours are a common and dangerous type of malignant tumour that, if not detected early enough, can cut short a patient's life. The segmentation and classification of brain tumours using solely traditional medical image processing is a difficult and time-consuming task. Various imaging modalities, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound image, are frequently utilized to assess brain, lung, liver, breast, prostate and other tumours. MRI images are specifically utilised in these analyses to detect brain tumours. As a result, developing approaches for detecting, recognizing, and classifying the conditions based on image analysis becomes essential. A comprehensive and automatic classification system is important to saving human lives. The geographical and anatomical heterogeneity of brain tumours makes automatic categorization challenging. This study proposes an automated method for detecting brain tumours using Convolutional Neural Networks (CNNs) classification, with the primary goal of developing a deep learning model that is capable of accurately identifying and classifying images as either having a brain tumour or not. In this paper, we provide a classification model for brain tumours based on a Deep Convolutional Neural Network with a vanilla neural network technique. The proposed method's performance on a publicly available dataset of 3000 Brain MRI Images yielded superior results, with accuracy and F1 score of 98.00 percent and 98.00 percent, respectively. This study shows that the proposed vanilla-CNN model can be used to make it easier for brain tumours to be automatically classified
References
“Brain Tumor: Statistics | Cancer.Net.” Accessed: Jun. 19, 2022. [Online]. Available: https://www.cancer.net/cancer-types/brain-tumor/statistics
“Brain tumours - NHS.” Accessed: Jun. 19, 2022. [Online]. Available: https://www.nhs.uk/conditions/brain-tumours/
N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, “Brain tumor classification using convolutional neural network,” IFMBE Proceedings, vol. 68, no. 1, pp. 183–189, 2019, doi: 10.1007/978-981-10-9035-6_33.
Q. T. Ostrom et al., “American Brain Tumor Association Adolescent and Young Adult Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2008-2012,” Neuro-Oncology, vol. 18, pp. i1–i50, 2015, doi: 10.1093/neuonc/nov297.
N. Gordillo, E. Montseny, and P. Sobrevilla, “State of the art survey on MRI brain tumor segmentation,” Magnetic Resonance Imaging, vol. 31, no. 8, pp. 1426–1438, 2013, doi: 10.1016/j.mri.2013.05.002.
L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11211 LNCS, pp. 833–851, 2018, doi: 10.1007/978-3-030-01234-2_49.
F. Milletari, N. Navab, and S. A. Ahmadi, “V-Net: Fully convolutional neural networks for volumetric medical image segmentation,” Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, pp. 565–571, 2016, doi: 10.1109/3DV.2016.79.
I. A. El Kader, G. Xu, 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, 2021, doi: 10.3390/brainsci11030352.
J. Yang, F. Xie, H. Fan, Z. Jiang, and J. Liu, “Classification for dermoscopy images using convolutional neural networks based on region average pooling,” IEEE Access, vol. 6, pp. 65130–65138, 2018, doi: 10.1109/ACCESS.2018.2877587.
W. Ayadi, W. Elhamzi, I. Charfi, and M. Atri, “Deep CNN for Brain Tumor Classification,” Neural Processing Letters, vol. 53, no. 1, pp. 671–700, 2021, doi: 10.1007/s11063-020-10398-2.
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–15, 2015.
“Br35H?:: Brain Tumor Detection 2020 | Kaggle.” Accessed: Jun. 19, 2022. [Online]. Available: https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection
T. Rahman and M. S. Islam, “MRI Brain Tumor Classification Using Deep Convolutional Neural Network,” in 2022 International Conference on Innovations in Science, Engineering and Technology, ICISET 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 451–456. doi: 10.1109/ICISET54810.2022.9775817.
H. A. Khan, W. Jue, M. Mushtaq, and M. U. Mushtaq, “Brain tumor classification in MRI image using convolutional neural network,” Mathematical Biosciences and Engineering, vol. 17, no. 5, pp. 6203–6216, 2020, doi: 10.3934/MBE.2020328.
S. Arora and M. Sharma, “Deep Learning for Brain Tumor Classification from MRI Images,” in Proceedings of the IEEE International Conference Image Information Processing, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 409–412. doi: 10.1109/ICIIP53038.2021.9702609.
C. Srinivas et al., “Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images,” J Healthc Eng, vol. 2022, 2022, doi: 10.1155/2022/3264367.
S. Divya, A. Ali, N. Ibrahim, and L. Padma Suresh, “Automated Models for the Classification of Magnetic Resonance Brain Tumour Images,” in ICAC 2023 - 28th International Conference on Automation and Computing, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICAC57885.2023.10275235.
M. Aamir et al., “A deep learning approach for brain tumor classification using MRI images,” Computers and Electrical Engineering, vol. 101, Jul. 2022, doi: 10.1016/j.compeleceng.2022.108105.
M. Ravinder et al., “Enhanced brain tumor classification using graph convolutional neural network architecture,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-41407-8.
P. Neelima, P. Nikilish, and R. S. Shankar, “Fine-Tuning based Deep Transfer Learning System used to Identify the Stage of Brain Tumour from MR-Images,” in Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 1003–1011. doi: 10.1109/ICAISS58487.2023.10250555.
S. Deepak and P. M. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Comput Biol Med, vol. 111, Aug. 2019, doi: 10.1016/j.compbiomed.2019.103345.
Z. N. K. Swati et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Computerized Medical Imaging and Graphics, vol. 75, pp. 34–46, Jul. 2019, doi: 10.1016/j.compmedimag.2019.05.001.
