A Medical Image Classification using ID3 Classifier

International Journal of Computer Science and Engineering
© 2016 by SSRG - IJCSE Journal
Volume 3 Issue 10
Year of Publication : 2016
Authors : P.Kanmani, Dr.P.Marikkannu, M.Brindha
: 10.14445/23488387/IJCSE-V3I10P105

pdf
Citation:
MLA Style:

P.Kanmani, Dr.P.Marikkannu, M.Brindha, "A Medical Image Classification using ID3 Classifier" SSRG International Journal of Computer Science and Engineering 3.10 (2016): 8-11.

APA Style:

P.Kanmani, Dr.P.Marikkannu, M.Brindha,(2016). A Medical Image Classification using ID3 Classifier. SSRG International Journal of Computer Science and Engineering 3.10, 8-11.

Abstract:

Segmentation and classification of brain tumors using Magnetic Resonance Imaging(MRI) and Computed Tomography(CT) is a difficult task due to the various complexity of tumors.This paper presents ID3 classifier with association rule mining for solving classification problems of the MRI images.The proposed method consists of mainly three stages, Preprocessing, Association Rule Mining and Classification.ID3 classifier is used for predicting classification based on sensitivity,specificity and accuracy.The main goal of this method is to achieve the higher accuracy rate and lower error rate.

References:

[1] Zeng, Hong, and Aiguo Song. "Optimizing Single-Trial EEG Classification by Stationary Matrix Logistic Regression in Brain-Computer Interface." (2015).
[2] Al-Shaikhli, Saif Dawood Salman, Michael Ying Yang, and Bodo Rosenhahn. "Brain tumor classification using sparse coding and dictionary learning." Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, 2014.
[3] Pereira, Sérgio, et al. "Brain Tumor Segmentation using Convolutional Neural Networks in MRI Images." (2016).
[4] Anitha, V., and S. Murugavalli. "Brain tumour classification using two-tier classifier with adaptive segmentation technique." IET Computer Vision 10.1 (2016): 9-17.
[5] Nandpuru, Hari Babu, S. S. Salankar, and V. R. Bora. "MRI brain cancer classification using support vector machine." Electrical, Electronics and Computer Science (SCEECS), 2014 IEEE Students' Conference on. IEEE, 2014.
[6] Jui, Shang-Ling, et al. "Brain MR image tumor segmentation with 3-Dimensional intracranial structure deformation features." (2015).
[7] Yang, Xiaofeng, and Baowei Fei. "A MR brain classification method based on multiscale and multiblock fuzzy C-means." Bioinformatics and Biomedical Engineering,(iCBBE) 2011 5th International Conference on. IEEE, 2011.
[8] Ibrahim, Walaa Hussein, Ahmed AbdelRhman Ahmed Osman, and Yusra Ibrahim Mohamed. "MRI brain image classification using neural networks." Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on. IEEE, 2013.
[9] Joshi, Dipali M., N. K. Rana, and V. M. Misra. "Classification of brain cancer using artificial neural network." Electronic Computer Technology (ICECT), 2010 International Conference on. IEEE, 2010.
[10] Othman, Mohd Fauzi Bin, Noramalina Bt Abdullah, and Nurul Fazrena Bt Kamal. "MRI brain classification using support vector machine." Modeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on. IEEE, 2011.
[11] Boberek, Marzena, and Khalid Saeed. "Segmentation of MRI brain images for automatic detection and precise localization of tumor." Image Processing and Communications Challenges 3. Springer Berlin Heidelberg, 2011. 333-341.
[12] Ji, Zexuan, et al. "Fuzzy local Gaussian mixture model for brain MR image segmentation." Information Technology in Biomedicine, IEEE Transactions on 16.3 (2012): 339-347.
[13] Dawngliana, Malsawm, et al. "Automatic brain tumor segmentation in MRI: Hybridized multilevel thresholding and level set." Advanced Computing and Communication (ISACC), 2015 International Symposium on. IEEE, 2015.

Key Words:

Image Segmentation, ID3, Association Rule mining, Classification