Classification and Detection of Glioblastoma Tumor from MRI Images

Brain tumor is a serious life-threatening disease. The early diagnosis of brain tumor is very important for the proper treatment. The removal of brain tumor requires implementation of an invasive procedure relying on biopsy and spinal tap method. There are more than 120 types of brain tumor. Therefore, the identification of a particular brain tumor is a very difficult task because of the small size and complex structure of the brain. A lot of research has been conducted to classify brain tumors into benign and malignant tumors but there is very limited research work available to classify brain tumor by its types. Among the various types of brain tumors is Glioblastoma which is very aggressive and life threatening. The main purpose of the proposed approach is accurate classification of brain tumor contained images followed by localization on those images. The proposed approach uses Convolutional Neural Networks (CNNs) for classification and Region-based Convolutional Neural Networks (R-CNN) and Faster Region-based Convolutional Neural Networks (Faster R-CNN) to localize tumor regions in MRI images after classification into three types, i.e. Glioblastoma, Dendroglioma and Astrocytoma. Moreover, the proposed approach involves the use of transfer learning techniques, namely feature extraction and fine tuning and achieves classification rate of 95.40% and detection rates of 89.10%.