Brain tumor diagnosis requires manual examination of MRI images by a radiologist. This process can be error-prone due to the complexities in the structure of a brain and time-consuming in developing regions with limited access to medical experts. In this project, we applied the concept of transfer learning to build a convolutional neural networks algorithm based on the mobileNetV2 model. This model can accurately and efficiently classify common brain tumor MRI images. Furthermore, it is portable to mobile devices which could help healthcare providers to automate the diagnostic process and patients to receive faster treatment.