Accurate Detection of Brain Tumor Using Compound Filter and Deep Neural Network

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The unexpected growth of nerves inside the human brain that interferes with the normal function of the brain is referred to as a brain tumor. Magnetic Resonance Imaging (MRI) is used to provide images of better resolution of the brain. This paper proposes a system that applies a compound filter, along with a Convolution Neural Network (CNN) and Support Vector Machine (SVM) for the detection of brain tumors in MRI. For finding tumors this proposed system has been divided into the following sections: preprocessing, segmentation, feature extraction and tumor detection. This system employs a compound filter for preprocessing that is made up of Gaussian, mean and median filters. Threshold and histogram-based techniques have been applied for image segmentation and Grey Level Co-occurrence Matrix (GLCM) for feature extraction. For tumor detection, the SVM and CNN classifiers were employed. CNN is a Deep Neural Network (DNN) based classifier. The tumor detection accuracy of CNN and SVM classifiers have been estimated at 98.06% and 93.28%, respectively. The proposed system concludes that the accuracy of CNN is superior to SVM.