A Review of Brain Tumor Detection in MRI Using Hybrid Fuzzy C-Means Clustering and Support Vector Machine Techniques
Keywords:
Brain tumor detection, Fuzzy C-Means clustering, Image segmentation, Machine learning, Magnetic resonance imaging, Support vector machine, Tumor classificationAbstract
The heterogeneous and complex nature of the brain tumors makes its detection and classification important to the accurate diagnosis, prognosis and treatment planning. The tumors are classified on the basis of their origin as either primary or secondary and on the basis of behavior as either being benign or malignant and each of these classifications has an effect on the clinical presentation and treatment options. The location of the tumor in the brain also has an additional impact on the symptoms, accessibility of surgery, and treatment. The modality of choice is Magnetic Resonance Imaging (MRI), which has all the advantages of high soft tissue contrast, multiplanar imaging, and functional applications, including diffusion-weighted imaging, perfusion MRI, and magnetic resonance spectroscopy. This work is based on the suggestion of a hybrid system, with Fuzzy C-Means (FCM) clustering used for segmentation and a Support Vector Machine (SVM) used as a classification tool. FCM is used to assign probabilistic pixel values to represent overlapping tissue properties, and SVM is used to classify segmented regions using features of intensity, texture, shape, and edges. The hybrid method improves the ability to detect and distinguish the types of tumors and helps radiologists identify and plan treatment much earlier and monitor brain tumors through ongoing procedures.
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