The classification of gliomas based on a Pyramid dilated convolution resnet model

Zhenyu Lu, Yanzhong Bai, Yi Chen, Chunqiu Su, Shanshan Lu, Tianming Zhan*, Xunning Hong, Shuihua Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)173-179
Number of pages7
JournalPattern Recognition Letters
Volume133
DOIs
Publication statusPublished - May 2020
Externally publishedYes

Keywords

  • Classification
  • Deep learning
  • Dilated convolution
  • Gliomas
  • ResNet

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