NDLI: A Classifier to Detect Tumor Disease in MRI Brain Images

Content Provider	IEEE Xplore Digital Library
Author	Al-Badarneh, A. Najadat, H. Alraziqi, A.M.
Copyright Year	2012
Description	Author affiliation: Comput. Sci. Dept., Jordan Univ. of Sci. & Technol., Irbid, Jordan (Alraziqi, A.M.) \|\| Comput. Inf. Syst. Dept., Jordan Univ. of Sci. & Technol., Irbid, Jordan (Najadat, H.)
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.
Sponsorship	IEEE Comput. Soc.
Starting Page	784
Ending Page	787
File Size	389308
Page Count	4
File Format	PDF
ISBN	9781467324977
DOI	10.1109/ASONAM.2012.142
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-26
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Brain Accuracy K-Nearest Neighbour (K-NN) Magnetic resonance imaging Imag classification Neural network (NN) Neurons Artificial neural networks Magnetic resonance imaging (MRI) Feature extraction Texture features
Content Type	Text
Resource Type	Article

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