Detecting Brain Mri Anomalies By Using Svm Classification

This research paper proposes an intelligent classification technique to identify anomalies present in brain MRI. The manual interpretation of anomalies based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of image slices after identifying abnormal MRI volume, for anomalies identification. In this research work, advanced classification techniques based on Support Vector Machines (svm) are proposed and applied to brain image classification using features derived. SVM is a artificial neural network technique used for supervised learning of classification. This classifier is compared with other pre store images for detecting the anomalies. From this analysis, The performance of svm classifier was evaluated in terms of classification accuracies and the result confirmed that the proposed method has potential in detecting the anomalies.

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