Fuzzy Clustering Approach for Brain Tumor Detection

Abstract

Abstract: This paper focuses on using a simple algorithm to detect the range and shape of tumors in brain MR images and to determine the stage of the tumor from the given tumor region. Tumors are uncontrolled tissue growth that can occur in any section of the body. Tumors come in a number of varieties, each tumor type has its own set of characteristics and treatment options. Because of its nature in confined space ofthe cerebral cavity, brain tumors are inherently dangerous and life-threatening (space formed inside the skull).According to the studies carried out in developed countries, an increasing number of people with brain tumors have perished as a result of faulty detection. In most cases, a CT scan or MRI focusing on the cerebral cavity yields a comprehensive view of the brain. Some general risk factors and symptoms was established after extensiveresearch and statistical analysis based on those people who are affected by brain tumors. The growth of technology in science day and night aims to provide new therapeutic approaches. The clinician examines this imaging method for the purpose of detecting and diagnosing a brain tumor. However, this method helps to accurately predict the size of the tumor, as well as the stage of the tumor from the tumor’s location is detected during the process. The k-means and fuzzy c-means are the algorithms that are used in this study to segment brain tumors from the given brain tumor image. This method enables the accurate and repeatable segmentation of tumor tissue, comparable to manual segmentation. Furthermore, it cuts down on analysis time and detects tumor stage from a particular tumor region. Finally, create a system that uses Java to identify the tumor stage thatis easier to use, less expensive, and saves time. Index Terms: Magnetic Resonance Imaging (MRI),Fuzzy Ap- proach, Interval Type-II Fuzzy Logic System (IT2FLS); Tumor identification. Pre-processing, K-means