Brain Tumor Segmentation and Classification in MRI using Clustering and Kernel-Based SVM

Abstract

Brain tumor and other nervous systems cancer are one of the leading causes of death for many patients. Magnetic resonance imaging (MRI) is the most important medical imaging modality for diagnosing brain tumors and other disorders in the brain. Manual evaluation of several MRI images by radiologists or experts for diagnosing brain tumors especially at early stages is a challenging task. Hence, this paper proposes an automated framework for the segmentation and classification of brain tumors using K-means clustering and kernel-based support vector machine (K-SVM). The major steps of the proposed framework consist of preprocessing, segmentation, feature extraction with selection, and classification. In the preprocessing step, the regions of interest (ROI) are extracted using skull stripping and a median filter. In the next step, the tumor is segmented using an optimized K-means algorithm. Further, discrete wavelet transform (DWT)-based texture features are used for feature extraction, and significant features are selected by principal component analysis (PCA). Finally, the kernel-based support vector machine (K-SVM) is used for the classification of brain tumor types into benign and malignant, with a dataset using 160 MRI images, consisting of 20 normal and 140 abnormal. Experimental findings demonstrated the efficacy of the proposed framework with 98.75% accuracy, 95.43% precision, and 97.65% recall. The simulation findings emphasize the importance of the proposed system as compared to state-of-the-art techniques in terms of coherence parameters and performance.