Brain Tumor Detection

Authors

  • Mahendrakumar Chaudhary Student, Department of Information Technology, Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India
  • Muskan Lamba Student, Department of Information Technology, Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India
  • Sanket Datir Student, Department of Information Technology, Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India
  • Reshma Gulwani Professor, Department of Information Technology, Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India

DOI:

https://doi.org/10.37591/(rrjobi).v8i1.1137

Keywords:

Brain tumor, pre-processing, SVM linear, SVM RBF, random forest, logistic regression, CNN, VGG-16, ResNet50, magnetic resonance imaging (MRI)

Abstract

The MRI, or magnetic resonance imaging, is one of the most common practices in detecting a brain tumor since it contains important information used to extensively scan the human brain’s internal anatomy. The treatment of any tumor depends entirely on the knowledge and expertise of the physician. This paper reflects the identification of brain tumors using different machine learning algorithms along with the convolution neural network (CNN) and transfer learning. The Histogram of Oriented Gradient (HoG) is used as a function descriptor in machine learning algorithms such as SVM Linear, SVM RBF, random forest, and logistic regression. To enhance brain tumor detection accuracy, a CNN model is combined with pre-trained transfer learning models VGG-16 and ResNet50. Based on experimental results, the logistic regression achieves 95.3% accuracy, while CNN, VGG-16, and ResNet50 attain 84.31%, 94%, and 93.7% accuracy, respectively, which is a compelling measure.

References

Sharma Komal, Kaur Akwinder, Gujral Shruti. Brain Tumor Detection based on Machine Learning Algorithms. Int J Comput Appl. 2014;103(1):7–11. doi: 10.5120/18036–6883.

Noreen Neelum, Palaniappan Sellappan, Qayyum Abdul, Ahmad Iftikhar, Imran Muhammad, Shoaib Muhammad. A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Xplore. 2020;8:55135–44. doi: 10.1109/ACCESS.2020.2978629.

Selvakumar J, Lakshmi A, Arivoli T. ’Brain Tumor Segmentation and Its Area Calculation in Brain MR Images using K-mean Clustering and Fuzzy C-Mean Algorithm’, IEEE-international conference on advances in engineering, science and management, ICAESM; 2012.

Gyorfi Agnes, Kovacs Levente, Szilagyi Laszlo IEEE International Conference on Systems, Man and Cybernetics (SMC); 2019.

Jia Zheshu, Chen Deyun. Brain Tumor Identification and Classification of MRI images using deep learning techniques. IEEE Access. 2020.

Deepak S, Ameer PM. Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med. 2019;111:103345. doi: 10.1016/j.compbiomed.2019.103345, PMID 31279167.

Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci. 2019;30:174–82. doi: 10.1016/j.jocs.2018.12.003.

Pashaei A, Sajedi H, Jazayeri N. Brain tumor classification via convolutional neural network and extreme learning machines. Proceedings of the 8th international conference computability Knowl. Eng (ICCKE); 2018. p. 314–319pp.

Pereira S, Meier R, Alves V, Reyes M, Silva CA. Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. Underst Interpreting Mach Learn Med Image Comput Appl. 2018:106–14. doi:

1007/978–3–030–02628–8_12. 10. Brain tumor statistics American Brain Tumor Association; Oct 2019 [online]. Available from: https://www.cancer.net/cancer-types/brain-tumor/statistics.

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Published

2021-06-28

Issue

Vol. 8 No. 1 (2021)

Section

Research Article

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