Investigations of brain tumor classification system of MRI images using texture features and machine learning algorithms

Abstract

Cancer is the second leading cause of death for both men and women in worldwide and is expected to become the leading cause of death in the next several decades. It has been shown that early detection and treatment of brain cancer are the most effective methods of reducing mortality. The rapid development in image processing and soft computing technologies have greatly enhanced the interpretation of medical images and contributed to early diagnosis. This accounts for 13% of all deaths for that year, making cancer a common threat to all families. Glioblastoma (GBM) is the most aggressive and common form of brain cancer in adults. GBM is characterized by poor survival, remarkably high tumor heterogeneity, and lack of effective therapies. The current standard of treatment is maximal surgical resection, followed by radiation, with concurrent adjuvant chemotherapy. In the medical imaging field, the stroke lesions and the cerebral tumor represent tricky cases since their accurate detection has a crucial influence on clinical diagnosis. In addition, the analysis and viewing expert are very limited compared to a large amount of MR images. Analyzing these images manually has several disadvantages as time-consuming. Moreover, it is very exhausting to keep a high level of concentration during the classification that gives rise to increase the false hit rate. Therefore, an automated system is required to analyze MR images, where CAD is a promising solution. newline