Improved Cascade R-CNN for Medical Images of Pulmonary Nodules Detection Combining Dilated HRNet

Shihuai Xu, Huijuan Lu, Minchao Ye, Ke Yan, Wenjie Zhu, Qun Jin

研究成果

1 被引用数 (Scopus)

抄録

Using Computer-aided Diagnostic (CAD) to analyze medical images is currently a focused area, and deep learning is widely used in the detection of pulmonary nodules in medical imaging. Current detection algorithms are effective in detecting large pulmonary nodules, but their detection effect on small nodules and micro-nodules is not ideal. In order to solve this problem, this paper uses high-resolution network (HRNet) as the backbone network of Cascade R-CNN to improve its detection accuracy on small targets. HRNet can preserve the information of small target nodules in the feature map with high resolution and obtain a finegrained feature map for the detection task. This paper also combines dilated convolution with HRNet and proposes an improved HRNet named dilated HRNet. Experiments on the LIDC-IDRI dataset show that the improved Cascade R-CNN increases the detection accuracy of pulmonary nodules, especially on small nodules.

本文言語English
ホスト出版物のタイトルProceedings of the 2020 12th International Conference on Machine Learning and Computing, ICMLC 2020
出版社Association for Computing Machinery
ページ283-288
ページ数6
ISBN(電子版)9781450376426
DOI
出版ステータスPublished - 2020 2 15
イベント12th International Conference on Machine Learning and Computing, ICMLC 2020 - Shenzhen, China
継続期間: 2020 2 152020 2 17

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference12th International Conference on Machine Learning and Computing, ICMLC 2020
国/地域China
CityShenzhen
Period20/2/1520/2/17

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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