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Brain Tumor Detection Using Image Processing and Machine Learning Algorithms

Abstract: The purpose of this paper is to implement a Simple Algorithm to detect the range and shape of a brain tumor in MR images. There are many types of tumors, all of which are uncontrolled growths of tissues in any part of the body. Tumors are classified according to their types and they are treated differently according to their characteristics. It is well known that brain tumors are inherently dangerous due to their location within the skull's limited space (the intracranial cavity). According to the majority of research done in developed countries, people who die due to brain tumors are often mistakenly diagnosed. Generally, Imaging of the brain produced by CT scans or MRIs directed into the intracranial cavity is considered complete. During visual examination of this image by the physician, a brain tumor is detected & diagnosed. However, this method of detection limits the ability to accurately determine the stage & size of the tumor. To avoid that, by combining two algorithms, this study uses computer-aided segmentation (detection) of brain tumors as a means of avoiding that issue. Through the use of this method, tumor tissues can be segmented with similar accuracy and reproducibility as that achieved through manual segmentation. This method can also reduce the analysis time. As a result, a tumor is extracted from the MR image and its location and shape determined, and the tumor's stage is calculated from the amount of area calculated from the cluster. Keywords: Abnormalities, Magnetic Resonance Imaging (MRI), Brain tumor, Pre-processing, K-means, Fuzzy C-means, Thresholding.