Abstract:

The goal of this chapter is to develop a method for automatically detecting and classifying brain tumors in MR scans. Pre-processing, tumor identification, picture segmentation, feature extraction, and template-based classification are the steps of the procedure. The fuzzy KNN technique is integrated with other prominent approaches to image processing, such as fuzzy C-means clustering and affine harmonic enhancement (AHE). The suggested method utilizes big MRI images to identify and categorize the brain tumor. Determines whether or not a tumor is present in the supplied picture based on the confidence level assigned to that image. Accuracy is improved over the current method, and efficiency in terms of detection and categorization is shown. Human brain tumors are defined by the abnormal growth of cells that often originate in the brain and spread to nearby structures including the brain's veins and nerves. Alterations in brain anatomy or behavior are two more names for a brain tumor. Brain tumors may be categorized as either benign, premalignant, or malignant. A benign tumor that grows slowly yet has a significant impact on brain tissue is called a "kindhearted tumor." A precancerous tumor is one that has not yet undergone the cancerous process that drives its development, and it may be treated with the right therapy. There is now no real cure for harmful tumors, which is why men continue to die from them.

Description:

Automatic Detection, MRI-Brain Tumor, fuzzy KNN technique, affine harmonic enhancement