Brain Tumor Detection and Classification by MRI using Hybrid Techniques with SVM Classifiers

A brain tumor is a rare occurrence where abnormal cells within the brain replicate and grow uncontrollably. This mass of tissue starts to form inside the skull, disrupting the normal functioning of the brain. It is crucial to detect these tumors early on, as they have the potential to develop into cancer. Diagnosis at an early stage is essential, and this is typically achieved through the use of MRI or CT scanned pictures. By detecting the tumor when it is as small as possible, medical professionals can initiate appropriate treatment and management strategies to mitigate any potential risks and complications associated with the tumor's growth. This research primarily focuses on finding and localizing tumor regions in the brain using MRI scans from patients. Pre-processing, segmentation, and classification are the three steps of the suggested approach. The pre-processing stage comprises converting the original image to grayscale and removing any noise that has snuck in. Segmentation process after pre-processing detect the tumor region in the MRI images. Feature extraction gives prominent attribute of the images which is used for classification purpose.