Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks

Abstract

Glioblastoma (GBM) is a stage IV aggressive malignant brain tumor, which is generally found in the cerebral hemispheres of the brain [1]. The treatment of GBM tumor is very difficult and cure is not possible in most of the cases. The treatment can only slow down the progress of the cancer and may reduce the symptoms and discomfort. The diagnosis of GBM contains neurological exams, imaging tests i.e. MRI and biopsy. The GBM tumor may occur at any age; however, the high majority of the patients are adults. The symptoms of GBM may include worse headache, nausea, vomiting and seizures (Fig. 1). Medical science is equipped with advanced devices and technology. MRI machines are able to capture high contrast images of the brain and other parts of the body. These MRI scans are very useful to diagnose and detect tumors and other defected cells. However, sufficient knowledge and experience is desirable in order to read and understand these MRI scans. Sometimes, unavailability of such trained people may delay the diagnosis process. Therefore, in order to automate the process, a classification model can be developed using machine learning methods. Artificial neural

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