A Classifier to Detect Tumor Disease in MRI Brain Images

Authors:
Amer Al-Badarneh;Hassan Najadat;Ali M. Alraziqi
Affiliations:
-;-;-
Venue:
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Year:
2012

Citing 6
Cited 0

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Abstract

The traditional method for detecting the tumor diseases in the human MRI brain images is done manually by physicians. Automatic classification of tumors of MRI images requires high accuracy, since the non-accurate diagnosis and postponing delivery of the precise diagnosis would lead to increase the prevalence of more serious diseases. To avoid that, an automatic classification system is proposed for tumor classification of MRI images. This work shows the effect of neural network (NN) and K-Nearest Neighbor (K-NN) algorithms on tumor classification. We used a benchmark dataset MRI brain images. The experimental results show that our approach achieves 100% classification accuracy using K-NN and 98.92% using NN.