Classification of Brain Tumor Using Neural Network

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Brain tumors classification in magnetic resonance imaging (MRI) is very important in medical diagnosis. Most of the current conventional diagnosis techniques are based on human experience in interpreting the MRI-scan for classification. This paper presents an automated method based on backpropagation neural network (BPNN) for classification of the MRI of a human brain. The proposed method utilizes wavelet transform (WT) as a feature extraction tool of the MRI.  The proposed method follows two steps: feature extraction and classification. WT is first employed for decomposing the image into different levels of approximate and detailed coefficients and then these coefficients are fed into a BPNN for further classification and tumor detection. The proposed method has been applied on several MRI scans, and the results showed an acceptable accuracy of classification.
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Brain Tumors Classification; Feature Extraction; Two-Dimensional Discrete Wavelet Transform; Back Propagation Neural Network

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