Classification of MRI Images Using Neural Network
Diagnosing a brain tumor usually begins with Magnetic Resonance Imaging (MRI). However, the complexity associated with the MRI system makes this task non-trivial. Especially, distinguishing between different types of tumors, i.e., Meningioma, Glioma and Pituitary is not easy and is highly subjective. To address this issue, we train a Convolutional Neural Network (CNN) using transfer learning to classifying MRI images into the mentioned tumor types. We used pre-trained models of VGG16 and VGG19 and re-trained them on the publicly available dataset of figshare. A comparison of the performance of these models reveals that VGG16 models achieves a maximum accuracy of 84.59%, whereas the maximum accuracy attained by VGG19 is 86.70%. Our experimental results demonstrate
that the task of classifying tumorous MRI images can be efficiently done with CNN.