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A Classifier to Detect Tumor Disease in MRI Brain Images

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3 Author(s)

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.

Published in:

Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on

Date of Conference:

26-29 Aug. 2012