Comparison of Different Artificial Neural Networks for Brain Tumour Classification via Magnetic Resonance Images

Author

Rehman, Yawar ; Azim, Fahad

Author_Institution

Dept. of Electron. Eng., NED Univ. of Eng. & Technol., Karachi, Pakistan

fYear

2012

fDate

28-30 March 2012

Firstpage

14

Lastpage

18

Abstract

Artificial Neural Network algorithms has been tested for the classification of patterns and best among them was implemented for the application of brain tumour classification as specified by World Health Organization standards via 2D MR images. The technique of Rajasekaran and Pai (sBAM) was found to give most successful results of classifying tumour into their correct classes. The computation time taken by sBAM was also less as compared with other algorithms. sBAM technique wasn´t tested on brain tumour MR images before but when it is subjected to test, it provided prominent results. The success rate of sBAM was also relatively high with its counterparts.

Keywords

biomedical MRI; classification; medical image processing; networked control systems; neural nets; standards; tumours; 2D MR images; World Health Organization standards; artificial neural networks; brain tumour classification; magnetic resonance images; sBAM; Algorithm design and analysis; Artificial neural networks; Biological neural networks; Brain modeling; Classification algorithms; Neurons; Tumors; Artificial neural network; Brain tumour classification; sBAM;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Modelling and Simulation (UKSim), 2012 UKSim 14th International Conference on

Conference_Location

Cambridge

Print_ISBN

978-1-4673-1366-7

Type

conf

DOI

10.1109/UKSim.2012.13

Filename

6205544