Brain Tumor Diagnose Applying CNN through MRI

MRI (Magnetic Resonance Imaging) is a powerful tool for doctors and researchers to begin diagnosing brain tumors in general. After the MRI detects a tumor, a common way to determine the type of the tumor is to look at the results from a sample of tissue after a biopsy or surgery. However, diagnosing the brain tumor type based on the sample tissue is time-consuming for brain tumor experts. There could also be some misdiagnosis if the brain tumor doctor is not experienced enough. Therefore, our research goal is to use deep learning technology for brain tumor detection and diagnosis based on the given brain MRI image. This research tried different deep learning models to test their efficiency and accuracy, including VGG16/19, AlexNet, GoogleNet, Resnet. We also implemented a YOLO model to snip the tumor's exact location in the MRI image and make graphical enhancements to improve classification accuracy. It turns out that the classification accuracy after using YOLO detection and graphic enhancement decreases. Some of our research results demonstrate that deep learning models can contribute to brain tumor diagnosis to a great extent, improving the accuracy and diagnosis speed, so it is possible to use a deep learning model for brain tumor diagnosis and further complex diseases in the future.

I. Introduction

The best technique for detecting brain tumors is magnetic resonance imaging (MRI). A large amount of image data is generated through the scans. However, a manual examination can be error-prone due to the complexities involved in brain tumors and their properties based on MRI.