CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Comparison Study of Various Machine Learning Models for Classifying Tumors in Brain MRI

by Parvathy Jyothi, Robert A. Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 47
Year of Publication: 2022
Authors: Parvathy Jyothi, Robert A. Singh
10.5120/ijca2022921875

Parvathy Jyothi, Robert A. Singh . A Comparison Study of Various Machine Learning Models for Classifying Tumors in Brain MRI. International Journal of Computer Applications. 183, 47 ( Jan 2022), 28-32. DOI=10.5120/ijca2022921875

@article{ 10.5120/ijca2022921875,
author = { Parvathy Jyothi, Robert A. Singh },
title = { A Comparison Study of Various Machine Learning Models for Classifying Tumors in Brain MRI },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2022 },
volume = { 183 },
number = { 47 },
month = { Jan },
year = { 2022 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number47/32248-2022921875/ },
doi = { 10.5120/ijca2022921875 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:20:15.033863+05:30
%A Parvathy Jyothi
%A Robert A. Singh
%T A Comparison Study of Various Machine Learning Models for Classifying Tumors in Brain MRI
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 47
%P 28-32
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Magnetic Resonance Imaging is a non-invasive tool used for exploring the internal physique of human body.Machine learning models play a vital role in diagnosing anomalies in early stages so that treatment procedure can be planned according to the category of tumor. In this paper, a comparison study is executed on various machine learning models to classify brain tumors in MR images. For conducting experiments, the data is collected from publicly available dataset. Principal Component Analysis (PCA)is used to extract features from the input brain MR images. The machine learning models classify the images into two categories namely Glioma tumor and Pituitary tumor.

References
  1. Byale, H., Lingaraju, G.M. and Sivasubramanian, S., 2018. Automatic segmentation and classification of brain tumor using machine learning techniques. International Journal of Applied Engineering Research, 13(14), pp.11686-11692.
  2. Johnson, D.R., Guerin, J.B., Giannini, C., Morris, J.M., Eckel, L.J. and Kaufmann, T.J., 2017. 2016 updates to the WHO brain tumor classification system: what the radiologist needs to know. Radiographics, 37(7), pp.2164-2180.
  3. Mohanaiah, P., Sathyanarayana, P. and GuruKumar, L., 2013. Image texture feature extraction using GLCM approach. International journal of scientific and research publications, 3(5), pp.1-5
  4. Brain tumor dataset Available online: https://www.kaggle.com/datasets
  5. Gumaei, A., Hassan, M.M., Hassan, M.R., Alelaiwi, A. and Fortino, G., 2019. A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access, 7, pp.36266-36273.
  6. Swati, Z.N.K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S. and Lu, J., 2019. Brain tumor classification for MR images using transfer learning and fine-tuning. Computerized Medical Imaging and Graphics, 75, pp.34-46.
  7. Khan, I.U., Akhter, S. and Khan, S., 2020, February. Detection and classification of brain tumor using support vector machine based gui. In 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 739-744). IEEE.
  8. Krishnakumar, S. and Manivannan, K., 2021. Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images. Journal of Ambient Intelligence and Humanized Computing, 12(6), pp.6751-6760.
  9. Kaplan, K., Kaya, Y., Kuncan, M. and Ertunç, H.M., 2020. Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Medical hypotheses, 139, p.109696.
  10. Ramaneswaran, S., Srinivasan, K., Vincent, P.M. and Chang, C.Y., 2021. Hybrid inception v3 XGBoost model for acute lymphoblastic leukemia classification. Computational and Mathematical Methods in Medicine, 2021.
  11. Csaholczi, S., Kovács, L. and Szilágyi, L., 2021, January. Automatic Segmentation of Brain Tumor Parts from MRI Data Using a Random Forest Classifier. In 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000471-000476). IEEE.
  12. Aszhari, F.R., Rustam, Z., Subroto, F. and Semendawai, A.S., 2020, March. Classification of thalassemia data using random forest algorithm. In Journal of Physics: Conference Series (Vol. 1490, No. 1, p. 012050). IOP Publishing.
  13. Chaplot, S., Patnaik, L.M. and Jagannathan, N.R., 2006. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical signal processing and control, 1(1), pp.86-92.
  14. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. and Wojna, Z., 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
  15. Polly, F.P., Shil, S.K., Hossain, M.A., Ayman, A. and Jang, Y.M., 2018, January. Detection and classification of HGG and LGG brain tumor using machine learning. In 2018 International Conference on Information Networking (ICOIN) (pp. 813-817). IEEE.
  16. Mall, P.K., Singh, P.K. and Yadav, D., 2019, December. Glcm based feature extraction and medical x-ray image classification using machine learning techniques. In 2019 IEEE Conference on Information and Communication Technology (pp. 1-6). IEEE.
  17. Ebied, H.M., 2012, May. Feature extraction using PCA and Kernel-PCA for face recognition. In 2012 8th International Conference on Informatics and Systems (INFOS) (pp. MM-72). IEEE.
  18. Magagula, X.G., Hamam, Y., Jordaan, J.A. and Yusuff, A.A., 2017, June. Fault detection and classification method using DWT and SVM in a power distribution network. In 2017 IEEE PES PowerAfrica (pp. 1-6). IEEE.
  19. Sawakare, S. and Chaudhari, D., 2014. Classification of brain tumor using discrete wavelet transform, principal component analysis and probabilistic neural network. Int J Res EmergSciTechnol, 1(6), pp.2349-761.
  20. Rathi, V.G.P. and Palani, S., 2012. A novel approach for feature extraction and selection on MRI images for brain tumor classification. CCSEA, SEA, CLOUD, DKMP, CS and IT, 5, pp.225-234.
Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine Random Forest Classifier Normalization Gray Level Co-occurrence Matrix Principal Component Analysis XGBoost Classifier

Powered by PhDFocusTM