Detection and Classification of Brain Tumor in MRI Images using Deep Convolutional Network

Brain tumor is a serious disease occurring in human being. Medical treatment process mainly depends on tumor types and its location. The final decision of neuro-specialists and radiologist for the tumor diagnosis mainly depend on evaluation of MRI (Magnetic Resonance Imaging) Images. The manual evaluation process is time-consuming and needs domain expertise to avoid human errors. To overcome this issue, Faster R-CNN deep learning algorithm was proposed for detecting the tumor and marking the area of their occurrence with Region Proposal Network (RPN). The selected MR image dataset consists of three primary brain tumors namely glioma, meningioma and pituitary. The proposed algorithm uses VGG-16 architecture as a base layer for both the region proposal network and the classifier network. Detection and classification results of the algorithm demonstrate that it is able to achieve an average precision of 75.18% for glioma, 89.45% for meningioma and 68.18% for pituitary tumor. As a performance measure, the algorithm achieved a mean average precision of 77.60% for all the classes.