MRI brain tumor detection and classification using KPCA and KSVM

Abstract In this paper, a classification technique for MRI brain tumor is presented and classified as normal, benign and malignant tumors from human brain images. The planned system consists of 4 stages namely, Pre-processing and Segmentation, Feature extraction with feature reduction and Classification. In the first stage, Pre-processing and Segmentation are worked out using the Threshold function. In the second stage, the features are obtained using Discrete wavelet transformation (DWT) related to MR Images. The third stage consists of Principal component analysis (KPCA) which is used to reduce the magnetic resonance image features to more essential features. The last stage is the Classification stage, where a classifier KSVM is employed to classify the infecting area in brain tumor. The results experimentally achieved good accuracy and identified the brain MR Images as normal and abnormal tissues. The planned technique is powerful and effective in detecting tumors compared with different existing frameworks.

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