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

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

Automated summary

The study evaluates seven deep convolutional neural network (CNN) models for brain tumor classification, highlighting the importance of neuroimaging in the diagnosis of central nervous system tumors. Using a dataset consisting of 7023 MRI images, the models are trained and evaluated with various preprocessing strategies. Results show that InceptionV3 model performs the best with an average accuracy of 97.12% on this dataset. The development of these techniques could benefit clinicians specializing in early detection of brain tumors.