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 mode...

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Published in:Electronics (Basel) 2023-02, Vol.12 (4), p.955
Main Authors: Gómez-Guzmán, Marco Antonio, Jiménez-Beristaín, Laura, García-Guerrero, Enrique Efren, López-Bonilla, Oscar Roberto, Tamayo-Perez, Ulises Jesús, Esqueda-Elizondo, José Jaime, Palomino-Vizcaino, Kenia, Inzunza-González, Everardo
Format: Article
Language:English
Subjects:
Artificial neural networks
Automatic classification
Brain
Brain cancer
Brain tumors
Central nervous system
Classification
Datasets
Diagnosis
Evaluation
Machine learning
Magnetic resonance imaging
Medical imaging
Neural networks
Tumors
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12040955