Journal
PATTERN RECOGNITION LETTERS
Volume 133, Issue -, Pages 173-179Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2020.03.007
Keywords
Gliomas; Classification; Deep learning; ResNet; Dilated convolution
Categories
Funding
- National Natural Science Foundation of China [61976117, 61601236, 61772277, 61701238, 61772274]
- NaturalScienceFoundationof Jiangsu Province [BK20191409, BK20180727]
- Key Projects of University Natural Science Fund of Jiangsu Province [19KJA360001]
- Collaborative Innovation Center of Audit Information Engineering and Technology [18CICA09]
- Open Foundation of Jiangsu Key Laboratory of Advanced Manufacturing Technology [HGAMTL-1703]
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Gliomas are characterized by high morbidity and high mortality in primary tumors. The identification of glioma type is helpful for radiologists to facilitate correct medical judgments and better prognosis for patients. In order to avoid harm to patients caused by a biopsy, radiologists attempt to classify Magnetic Resonance Images(MRI) using deep learning methods. In the present paper, we propose a deep learning convolutional neural network ResNet based on the pyramid dilated convolution for Gliomas classification. The pyramid dilated convolution is integrated into the bottom of Resnet to increase the receptive field of the original network and improve the classification accuracy. After adding the pyramid dilated convolution model, the receptive field of the original network underlying convolution was improved. A clinical dataset is used to test the pyramid dilated convolution ResNet neural network model proposed in this paper. The experimental results demonstrate that the proposed method can effectively improve glioma classification performance. (C) 2020 Elsevier B.V. All rights reserved.
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