research-article

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

Authors:

Qun JinAuthors Info & Claims

ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

Pages 283 - 288

https://doi.org/10.1145/3383972.3384070

Published: 26 May 2020 Publication History

Abstract

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.

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Cited By

Yang LWan YPan F(2025)Enhancing Chest X-ray Diagnosis with a Multimodal Deep Learning Network by Integrating Clinical History to Refine AttentionJournal of Imaging Informatics in Medicine10.1007/s10278-025-01446-1Online publication date: 19-Feb-2025
https://doi.org/10.1007/s10278-025-01446-1
Hao SLi XPeng WFan ZJi ZGanchev I(2024)YOLO-CXR: A Novel Detection Network for Locating Multiple Small Lesions in Chest X-Ray ImagesIEEE Access10.1109/ACCESS.2024.348210212(156003-156019)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3482102
Ren XChu SJi GZhao ZZhao JQiang YWei YWang Y(2024)OMSF2: optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentationComplex & Intelligent Systems10.1007/s40747-024-01729-011:1Online publication date: 30-Dec-2024
https://doi.org/10.1007/s40747-024-01729-0
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Index Terms

Improved Cascade R-CNN for Medical Images of Pulmonary Nodules Detection Combining Dilated HRNet
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

February 2020

607 pages

ISBN:9781450376426

DOI:10.1145/3383972

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 May 2020

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Natural Science Foundation of Zhejiang Province
National Natural Science Foundation of China

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ICMLC 2020

ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing

February 15 - 17, 2020

Shenzhen, China

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Cited By

Yang LWan YPan F(2025)Enhancing Chest X-ray Diagnosis with a Multimodal Deep Learning Network by Integrating Clinical History to Refine AttentionJournal of Imaging Informatics in Medicine10.1007/s10278-025-01446-1Online publication date: 19-Feb-2025
https://doi.org/10.1007/s10278-025-01446-1
Hao SLi XPeng WFan ZJi ZGanchev I(2024)YOLO-CXR: A Novel Detection Network for Locating Multiple Small Lesions in Chest X-Ray ImagesIEEE Access10.1109/ACCESS.2024.348210212(156003-156019)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3482102
Ren XChu SJi GZhao ZZhao JQiang YWei YWang Y(2024)OMSF2: optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentationComplex & Intelligent Systems10.1007/s40747-024-01729-011:1Online publication date: 30-Dec-2024
https://doi.org/10.1007/s40747-024-01729-0
Yuan YLiu LYang XLiu LHuang Q(2024)Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease DetectionJournal of Imaging Informatics in Medicine10.1007/s10278-024-01133-737:6(2752-2767)Online publication date: 17-May-2024
https://doi.org/10.1007/s10278-024-01133-7
Xu ZZhang XZhang HLiu YZhan YLukasiewicz T(2023)EFPN: Effective medical image detection using feature pyramid fusion enhancementComputers in Biology and Medicine10.1016/j.compbiomed.2023.107149163(107149)Online publication date: Sep-2023
https://doi.org/10.1016/j.compbiomed.2023.107149
Liu HJiao MYuan YOuyang HLiu JLi YWang CLang NQian YJiang LYuan HWang X(2022)Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRIInsights into Imaging10.1186/s13244-022-01227-213:1Online publication date: 10-May-2022
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Shao WZeng HGao YZhang KZheng R(2021)Measurement of Spinous Process Angles on Ultrasound Spine Images using HR-Net Method2021 IEEE International Ultrasonics Symposium (IUS)10.1109/IUS52206.2021.9593791(1-4)Online publication date: 11-Sep-2021
https://doi.org/10.1109/IUS52206.2021.9593791

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