CNN-based MRI Brain Tumor Detection Application

Brain tumors are usually diagnosed manually by the doctors from the Magnetic Resonance Images, which decreases the efficiency of the diagnosis process. Facing the situation that diagnosis of brain tumors from Magnetic Resonance Images needs effective methods to increase the speed and enhance the accuracy, we proposed algorithms using Convolutional Neural Network, the MobileNet, and AlexNet models to help classify the tumor while also developed an interface system to connect the algorithm directly to hospital system. We utilized grouped dataset and developed the algorithm to classify whether there is brain tumor occurred in the Magnetic Resonance images. The patients can employ the interface system developed through Tkinter by simply typing the information and automatically get the final results appears on the screen. From our result, compared with other models such as MobileNet and AlexNet, the proposed Convolutional Neural Network algorithm reaches the highest accuracy and lowest loss. Our interface system enables the patients of the hospital to directly and conveniently access the diagnosis of our algorithm.