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The conventional method for medical resonance brain images and classification and tumor detection is done by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are non-reproducible. Hence Neural Network techniques for classification of the magnetic resonance human brain images. The proposed Neural network technique consists of the following stages namely, feature extraction, dimensionality reduction, and classification. The features extracted from the magnetic resonance images (FMRI) have been reduced using independent component analysis (ICA) to the more essential features and in classification stage, classifier based on back-propagation neural network has been developed. This classifier has been used to classify subjects as normal, benign and malignant brain tumor images. The results show that BPN classifier gives fast and accurate classification than the other neural networks and can be effectively used for classifying brain tumor with high level of accuracy.


Course Curriculum

    • Brain Tumor Detection Reference Paper 00:00:00
    • Brain Tumor Detection Synopsis 00:00:00
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