Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning

Abstract

Medical image processing is a rapidly growing and concentrating topic today. Medical image analysis techniques are used to diagnose and cure illnesses. One such fundamental and potentially fatal illness is brain tumor, which is an abnormal growth of brain cells within the brain. Due to the complexity of the brain's anatomy. To improve efficiency and reduce the complexity of the picture segmentation process, this work investigated computer tomography (CT)-based brain tumor segmentation. CT scans are often used to diagnose head traumas, malignancies, and skull fractures. The images from the brain tumor database are evaluated in this study effort, and a preprocessing approach called adaptive median filter is used to increase the image's clarity. The preprocessing stage eliminates noise and high-frequency artifacts from the pictures. The median filter is a type of nonlinear digital filter commonly used to reduce noise in a picture or signal. Regardless of the preprocessing technique used, feature extraction techniques are updated, and then classification procedures such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) classifier are applied to the picture to classify it as normal or abnormal. Following classification, aberrant images are tracked and selected for segmentation using the Fuzzy C-Means (FCM) clustering technique and associated optimization techniques. In the suggested technique, centroid optimizations such as Grey Wolf Optimization (GWO) and Social Spider Optimization (SSO) combined with a Genetic Algorithm (GA) are utilized to improve the accuracy of the FCM centroid. The suggested work produces the most extreme execution in tumour picture segmentation evaluation appears differently from other works. The conclusion indicates that the hybrid technique (SSO-GA) obtains the highest accuracy of 99.24% compared to other individual algorithms. MATLAB 2014 is utilized to implement the brain tumor classification and segmentation algorithms in this research effort.