Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

Real Brain Tumors Datasets Classification using TANNN

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Eman M. Ali, Ahmed F. Seddik, Mohamed H. Haggag
10.5120/ijca2016910667

Eman M Ali, Ahmed F Seddik and Mohamed H Haggag. Real Brain Tumors Datasets Classification using TANNN. International Journal of Computer Applications 146(4):8-14, July 2016. BibTeX

@article{10.5120/ijca2016910667,
	author = {Eman M. Ali and Ahmed F. Seddik and Mohamed H. Haggag},
	title = {Real Brain Tumors Datasets Classification using TANNN},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2016},
	volume = {146},
	number = {4},
	month = {Jul},
	year = {2016},
	issn = {0975-8887},
	pages = {8-14},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume146/number4/25384-2016910667},
	doi = {10.5120/ijca2016910667},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Cancerous tumors considered being one of the acute diseases that cause the human death especially brain cancers.

Many computer-aided diagnosis systems are now widely spread to aid in brain tumors diagnosis. Therefore, an automated and reliable computer-aided diagnostic system for diagnosing and classifying the brain tumor has been proposed [1].

MRI (Magnetic resonance Imaging) is one source of brain tumors detection tools, but using MRI in children brain tumors classification is considered to be difficult process according to the variance and complexity of tumors. This paper presents a survey of the most famous techniques used for the classification of brain tumors based on children MRI [2].

The brain tumors detection and classification systems consist of four stages, namely, MRI preprocessing, Segmentation, Feature extraction, and Classification stages respectively. In the first stage, the main task is to eliminate the medical resonance images (MRI) noise which may cause due to light reflections or any inaccuracies in the imaging medium.

The second stage, which is the stage where the region of interest is extracted (tumor region). In the third stage, the features related to MRI images will be obtained and stored in an image vector to be ready for the classification process. And finally the fourth stages, where classifier will take place to specify the tumor kinds.

TANNN is a new classification technique user to get a very high performance compared with other classification techniques such as KNN, SVM, DT, and Naïve Bayes.

Image classification is an important task in the image processing and especially in the medical diagnosis field. Image classification refers to the process of labeling images into one of a number of predefined categories. In this survey, the test of various classification techniques against each other will be present.

References

  1. E. M. Ali, A. F. Seddik. M. H. Haggag, "Using Data Mining Techniques for children Brain Tumors classification based on MRI", International Journal of Computer applications , Pp. 36-42, Vol. 131 , No. 2 , December 2015.
  2. E. M. Ali, A. F. Seddik. M. H. Haggag, "Classification of Hydrocephalus using TAN", International Journal od Advanced Research in Computer Science and Software Engineering, Pp. 90-97, Vol. 5 , Issue. 11, November 2015.
  3. A. Rajkumar, "A Multi- Stage Hybrid , CAD Approach for MRI Brain Tumor Recognition and Classification", The IIOAB Journal School of Computing Science and Engineering VIT University, Januar 2016, India.
  4. S. Ganesh, "A Comparative Study on Various Brain Tumor Classification Methods", India Journal of Engineering , Vol. 13 , Pp. 27-33, January 2016.
  5. K. Sakthivel, B. Swathi, S. Vishnu, "Analysis of Medical Image Processing and it's Application in healthcare", International Journal od Advanced Engineering Research and Science (IJAERS), Vol. 3 Issue 2, Feb. 2016.
  6. Y. Li , Y. Mingquan, Z. Hao, "Tumor Diagnosis Based on the GMM Feature Decision Classification of Brain MR Images", International journal of Multimedia and Ubiquitous Engineering , Vol. 11 No. 3 , Pp. 37- 44, 2016.
  7. K. Mayuri, R. Khode, S. Salwe, "A review on efficient Brain Tumor Detection Using Various Methods", International Journal of Research in Advent Technology , Vol. 4 , No. 2 , February 2016.
  8. L. Hou, D. Samaras, T. Kurc , " Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification", ARXIV , March 2016.
  9. N. Kasat, S. Thepade, "Novel Content Based Image Classification Method Using LBG Vector Quantization Method with Bayes and Lazy Family Data Mining Classifier", 7 th international conference on Communication , Computing and Virtualization, Pp. 483 – 489, 2016.
  10. G. Santhosh , K. Sivanaruleselvan, P. Betty, " Survey on Brain Tumour Detection and Classification Using Image Processing", ELK ASIA Pacific Journal of Computer Science and Information Systems, Vol. 2, Issue. 1, 2016.
  11. A. Mali, S. Pawar, "Detection & Classification of Brain Tumor", International Journal of Innovative Research in Computer and Communication Engineering, Vol 4, Issue 1 , January 2016.
  12. K. Selva , P. Geetha, "Semantic Feature Based Classification of Brain MRI using PCA and PNN", International Conference on Electrical , Electronics, and Optimization Techniques (ICEEOT), 2016.
  13. BRATS brain tumor dataset,www.braintumorsegmentation.org
  14. NBTR brain tumor dataset , www.nbtr.nhs.uk
  15. OASIS brain tumor dataset, www.oasis-brains.org/
  16. ADNI brain tumor dataset, www.adni.loni.ucla.edu/

Keywords

Brain Tumor, MRI, Image Classification, Naïve Bayes, Decision Tree, Support Vector Machine, k-Nearest Neighbor.