Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks

The study of neuroimaging is a very important tool in the diagnosis central nervous system tumors. This paper presents evaluation seven deep convolutional neural network (CNN) models for task brain tumor classification. A generic CNN model implemented and six pre-trained are studied. For this proposal, dataset utilized Msoud, which includes Fighshare, SARTAJ, Br35H datasets, containing 7023 MRI images. magnetic resonance imaging (MRI) belongs to four classes, three tumors, including Glioma, Meningioma, Pituitary, one class healthy brains. trained with input images several preprocessing strategies applied paper. evaluated Generic CNN, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, EfficientNetB0. In comparison all models, best was obtained an average Accuracy 97.12%. development these techniques could help clinicians specializing early detection