Multi-Modal Case Study on MRI Brain Tumor Detection Using Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Temporal Convolution & Transfer Learning

Main Article Content

Partha Sutradhar
Prosenjit Kumer Tarefder
Imran Prodan
Md. Sheikh Saddi
Victor Stany Rozario

Abstract

In the Medical field, Brain Tumor Detection has become a critical and demanding task because of its several shapes, locations, and intensity of image. That’s why an automated system is important to aid physicians and radiologists in detecting and classifying brain tumors. In this research, we have discussed different machine learning as well as deep learning algorithm which are mostly used for image classification. We have also compared different models that are being used for tumor classification based on machine learning and deep learning. MRI images of Glioma tumor, Pituitary tumor, Meningioma tumor are the base of this research, and we have compared different techniques along with the accuracy of different classification models using those MRI images. We have used different deep learning pre-trained models for training the brain tumor images. Those pre-trained models have provided outstanding performance along with less power consumption and computational time. EfficientNet-B3 has provided the best accuracy of 98.16% among other models as well as traditional machine learning algorithms. The experimental result of this model is proven the best and most efficient for tumor detection and classification in comparison with other recent studies.

Article Details

How to Cite
[1]
SutradharP., TarefderP. K., ProdanI., SaddiM. S., and RozarioV. S., “Multi-Modal Case Study on MRI Brain Tumor Detection Using Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Temporal Convolution & Transfer Learning”, AJSE, vol. 20, no. 3, pp. 107 - 117, Sep. 2021.
Section
Articles

References

[1] Neelum Noreen1, Sellapan Palaniappan1, Abdul Qayyum2, Iftikhar Ahmad3 and Madini O. Alassaf “Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method” Vol.67, No.3, pp. 3967-3982, 2021
[2] M. Sajjad, S. Khan, K. Muhammad, W. Wu, A. Ullah et al., “Multi-grade brain tumor classification using deep CNN with extensive data augmentation,” Journal of Computational Science, vol. 30, pp. 174– 182, 2019
[3] El-Dahshan, E.S.A., Hosny, T., Salem, A.B.M., “Hybrid intelligent techniques for MRI brain images classification”, Digital Signal Processing, Elsevier, vol. 20, no. 2, pp.433-441, 2010.
[4] A. Sengur, “An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases”, Comp. Biol. Med. (2007).
[5] M. O’Farrell, E. Lewis, C. Flanagan, N. Jackman, “Comparison of k-NN and neural network methods in the classification of spectral data from an optical fibre-based sensor system used for quality control in the food industry”, Sens. Actuators B: Chemical 111–112C (2005) 354–362.
[6] Maitra, M., Chatterjee, A., Matsuno, F., “A novel scheme for feature extraction and classification of magnetic resonance brain images based on plantlet transform and support vector machine”, In Proceedings of International Conference on Instrumentation, Control and Information Technology, pp.1130-1134, 2008.
[7] G. Singh and M. Ansari, “Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram,” in 2016 1st India Int. Conf. on Information Processing, Delhi, India, pp. 1– 6, 2016.
[8] Provost F, Hibert C, Malet J P, et al. “Automatic classification of endogenous seismic sources within a landslide body using random forest algorithm” [C]//EGU General Assembly Conference Abstracts. 2016, 18: 15705.
[9] Qiong Ren, Hui Cheng, Hai Han, “Research on Machine Learning Framework Based on Random Forest Algorithm”, AIP Conference Proceedings 1820, 080020 (2017).
[10] Prachi Damodhar Shahare, Ram Nivas Giri, “Comparative Analysis of Artificial Neural Network and Support Vector Machine Classification for Breast Cancer Detection”, International Research Journal of Engineering and Technology (IRJET), vol-02, issue-09,Dec 2015.
[11] Rohith Gandhi, “Support Vector Machine- Introduction to Machine Learning Algorithm”, 2018.
[12] M. W. Libbrecht and W. S. Noble, “Machine learning applications in genetics and genomics,” Nature Reviews Genetics, vol. 16, no. 6, pp. 321–332, 2015.
[13] S. S. Nikam, “A comparative study of classification techniques in data mining algorithms,” Oriental journal of computer science & technology, vol. 8, no. 1, pp. 13–19, 2015.
[14] Jason Brownlee, “ A Gentle Introduction to Transfer Learning for Deep Learning”, 2017.
[15] Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, and Ronald M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning”, IEEE Trnasctions on Medical Imaging, vol. 35, no. 5, MAY 2016.
[16] Colin Lea, Michael D. Flynn, Rene Vidal, Austin Reiter, Gregory D. Hager, “Temporal Convolutional Networks for Action Segmentation and Detection”, 2016.
[17] Yangdong He, Jiabao Zhao, “Temporal Convolutional Networks for Anomaly Detection in Time Series”, Journal of Physics: Conference series, vol-1213, issue-4, 2019.
[18] Badža MM, Barjaktarović MC (2020), “Classification of brain tumors from MRI images using a convolutional neural network”, Appl Sci 10(6):1–13.
[19] Çinar A, Yildirim M (2020), “Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture”, Med Hypotheses 139:109684.
[20] Z. N. K. Swati, Q. Zhao, M. Kabir, F. Ali, Z. Ali et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Computerized Medical Imaging and Graphics, vol. 75, pp. 34– 46, 2019.
[21] M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in European conference on computer vision, 2014, pp. 818-833.
[22] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.