Brain Tumor Detection and Classification of MR Images Using Texture Features and Fuzzy SVM Classifier

TLDR: A hybrid algorithm for detection brain tumor in Magnetic Resonance images using statistical features and Fuzzy Support Vector Machine (FSVM) classifier and the result shows that the proposed technique is robust and effective compared with other recent works.

Abstract: In this study we have proposed a hybrid algorithm for detection brain tumor in Magnetic Resonance images using statistical features and Fuzzy Support Vector Machine (FSVM) classifier. Brain tumors are not diagnosed early and cured properly so they will cause permanent brain damage or death to patients. Tumor position and size are important for successful treatment. There are several algorithms are developed for brain tumor detection and classifications in the field of medical image processing. The proposed technique consists of four stages namely, Noise reduction, Feature extraction, Feature reduction and Classification. In the first stage anisotropic filter is applied for noise reduction and to make the image suitable for extracting features. In the second stage, obtains the texture features related to MRI images. In the third stage, the features of magnetic resonance images have been reduced using principles component analysis to the most essential features. At the last stage, the Supervisor classifier based FSVM has been used to classify subjects as normal and abnormal brain MR images. Classification accuracy 95.80% has been obtained by the proposed algorithm. The result shows that the proposed technique is robust and effective compared with other recent works.