An Automated and Improved Brain Tumor Detection in Magnetic Resonance Images - et. al., Dr.G.Nallasivan, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The segmentation, identification, as well as extraction of tumor areas from magnetic resonance (MR) images is a major issue. It is a crucialas well as time-consuming process that radiologists and physicians must perform, and the precision is solely dependent on their expertise. As a result, the uses of image processing approaches become increasingly important in order to resolve these constraints. In this approach, an efficient detection of brain tumor is proposed for improving performance and minimizing complexity involved in the processing of medical images. The adaptive mean filter is utilized for pre-processing and the brain tumor image segmentation depends on Fuzzy C means algorithm. Subsequently, related features are extracted from every segmented tissue by GLCM method and the optimal features are selected by fuzzy concepts. For improving the accuracy as well as quality rate, Artificial Neural Networks (ANN)are utilized for classification. The input MR brain images are obtained from the database of Harvard medical school and efficient detection of brain tumor is performed.