Multi-Classification of Brain Tumor Images Using Deep Neural Network

Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiatio...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.69215-69225
Main Authors: Sultan, Hossam H., Salem, Nancy M., Al-Atabany, Walid
Format: Article
Language:English
Subjects:
Artificial neural networks
Brain
Brain cancer
Brain tumor
Cancer
Classification
convolutional neural network
Convolutional neural networks
data augmentation
Datasets
deep learning
Feature extraction
Image classification
Image contrast
Image enhancement
Image quality
Image segmentation
Imaging techniques
Ionizing radiation
Machine learning
Magnetic resonance imaging
Medical imaging
MRI
Neural networks
Task analysis
Training
Tumors
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Summary:Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently showed a remarkable performance, especially in classification and segmentation problems. In this paper, a DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV). The datasets include 233 and 73 patients with a total of 3064 and 516 images on T1-weighted contrast-enhanced images for the first and second datasets, respectively. The proposed network structure achieves a significant performance with the best overall accuracy of 96.13% and 98.7%, respectively, for the two studies. The results indicate the ability of the model for brain tumor multi-classification purposes.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2919122