Feasibility Evaluation of Brain Tumor Magnetic Resonance Imaging Classification Using Convolutional Neural Network Model

Magnetic resonance imaging (MRI) offers information about soft tissues using high-resolution imaging and is widely used for diagnosing conditions such as brain tumors. The purpose of this study was to verify the MRI brain tumor classification function through the convolutional neural network in artificial intelligence. This study used 3,264 MRI datasets which were divided into four classes. To learn artificial intelligence, the training and test datasets were set at 9:1, and 10% of the training dataset was designated as the validation dataset. The convolutional neural network (CNN) was composed of basic CNN and VGG16. Learning was verified through accuracy and loss. The accuracy of the classification function was confirmed through the generated models. There was no overfitting, and the classification function of basic CNN and VGG16 was found to be 67% and 80%, respectively. The MRI brain tumor classification results obtained in this study can be used as the basic data to graft MRI with artificial intelligence.