ABSTRACT
Glioma is a primary brain tumor disease that often attacks adults. Gliomas are related to nerve cells and other tissue infiltrations. Based on the level of spread in the brain, this disease is divided into high-grade and low-grade glioma. Much research has discussed this disease, but research related to early detection of glioma remains limited. In this study, we developed a segregation method for an area-based CT scan of a brain with low-grade glioma. The initial process needed to produce area-based image segmentation was preprocessing using a threshold, then the image segmentation process used fuzzy classification with three entropies. We consulted a neurologist to get information about the CT scan of the area of the brain with glioma. The final result of this study was segmentation of the CT scan. The results of this segmentation can be used by specialists to detect early glioma.
