An Efficient Classification System for Brain Tumor Based on Convolutional Neural Network

A brain tumor is a fatal disease that affects children and adults The disease might be detected using a physical exam or a neurological exam, but for the classification, it is done with biopsy. That last one is concerned with brain surgery, which is very hard and complicated in itself. Early detection and classification could help to choose the perfect plan for treatment. With the great development and change in technology, DL techniques could help in diagnosis and classification without any huge risks. Using the available data of Magnetic Resonance Imaging (MRI), that is studied by the radiologist, In our study, we took two approaches, the first including a transfer learning model and the second including a Convolutional Neural Network (CNN) model, to both classify different types of brain tumors. With the CNN approach, we managed to achieve an accuracy of 90 %. The experimental results show that our proposed CNN gives the best accuracy as compared to the transfer learning model.