Diagnosing the Abnormalities in Brain Tumors with the Technique of K-Means Clustering with Knowledge Acquisition

A tumor-tumor is a mass of abnormal cells in the body out of control, raising the pressure inside the skull known as pulmonary hypertension. Human brain tumors have recently emerged as one of the leading causes of death for a large number of people. The much more difficult and cutting-edge field is medical image processing, particularly when it comes to using neuroimaging (MRI) to find brain cancers in people. Furthermore, early discovery can treat serious conditions and save lives. Additionally, classified into non imaging method that generates high-quality MR images that are ideal for detecting aberrant growth, such as a brain tumor. This study suggested a model for detecting brain cancers that combines the K-means algorithms and an enhanced perceptron classifier. It demonstrates an effective technique for automatically segmenting brain tumors to remove tumors in mice from MR images. For greater performance, segmentation is done in this procedure utilizing the K-means-based approach. When compared to other clustering protocols, this improves the tumor borders more and is quite quick. The suggested method yields great outcomes.