TY - GEN
T1 - Improved Cascade R-CNN for Medical Images of Pulmonary Nodules Detection Combining Dilated HRNet
AU - Xu, Shihuai
AU - Lu, Huijuan
AU - Ye, Minchao
AU - Yan, Ke
AU - Zhu, Wenjie
AU - Jin, Qun
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China under Grants No.61272315, No.61701468, the Natural Science Foundation of Zhejiang Province under Grants No.LQ20F030015 and the outstanding student achievement cultivation program of China Jiliang University (2019YW16).
Publisher Copyright:
© 2020 ACM.
PY - 2020/2/15
Y1 - 2020/2/15
N2 - 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.
AB - 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.
KW - Cascade R-CNN
KW - HRNet
KW - Medical images
KW - dilated convolution
KW - pulmonary nodules detection
UR - http://www.scopus.com/inward/record.url?scp=85085911253&partnerID=8YFLogxK
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U2 - 10.1145/3383972.3384070
DO - 10.1145/3383972.3384070
M3 - Conference contribution
AN - SCOPUS:85085911253
T3 - ACM International Conference Proceeding Series
SP - 283
EP - 288
BT - Proceedings of the 2020 12th International Conference on Machine Learning and Computing, ICMLC 2020
PB - Association for Computing Machinery
T2 - 12th International Conference on Machine Learning and Computing, ICMLC 2020
Y2 - 15 February 2020 through 17 February 2020
ER -