research-article

Pulmonary Nodule Detection Based on ISODATA-Improved Faster RCNN and 3D-CNN with Focal Loss

Authors:

Arun Kumar Sangaiah,

Xinyi YangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 1s

Article No.: 36, Pages 1 - 9

https://doi.org/10.1145/3365445

Published: 17 April 2020 Publication History

Abstract

The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection system based on convolutional neural networks (CNN). Our system consists of two stages, pulmonary nodule candidate detection and false positive reduction. For candidate detection, we introduce Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to Faster Region-based Convolutional Neural Network (Faster R-CNN) model. For false positive reduction, a three-dimensional convolutional neural network (3D-CNN) is employed to completely utilize the three-dimensional nature of CT images. In this network, Focal Loss is used to solve the class imbalance problem in this task. Experiments were conducted on LUNA16 dataset. The results show the preferable performance of the proposed system and the effectiveness of using ISODATA and Focal loss in pulmonary nodule detection is proved.

References

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Cited By

Han HJiang BShi YJiang PZhang HShang CSun YLi YXiang D(2023)Response of the Northwest Indian Ocean purpleback flying squid (Sthenoteuthis oualaniensis) fishing grounds to marine environmental changes and its prediction model construction based on multi-models and multi-spatial and temporal scalesEcological Indicators10.1016/j.ecolind.2023.110809154(110809)Online publication date: Oct-2023
https://doi.org/10.1016/j.ecolind.2023.110809
Jenkin Suji RBhadauria SWilfred Godfrey W(2023)A survey and taxonomy of 2.5D approaches for lung segmentation and nodule detection in CT imagesComputers in Biology and Medicine10.1016/j.compbiomed.2023.107437165(107437)Online publication date: Oct-2023
https://doi.org/10.1016/j.compbiomed.2023.107437
Nguyen CNguyen LTran G(2022)DATA-CENTRIC DEEP LEARNING METHOD FOR PULMONARY NODULE DETECTIONJournal of Computer Science and Cybernetics10.15625/1813-9663/38/3/1722038:3(229-243)Online publication date: 22-Sep-2022
https://doi.org/10.15625/1813-9663/38/3/17220
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Index Terms

Pulmonary Nodule Detection Based on ISODATA-Improved Faster RCNN and 3D-CNN with Focal Loss
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 16, Issue 1s

Special Issue on Multimodal Machine Learning for Human Behavior Analysis and Special Issue on Computational Intelligence for Biomedical Data and Imaging

January 2020

376 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3388236

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2020

Accepted: 01 October 2019

Revised: 01 August 2019

Received: 01 June 2019

Published in TOMM Volume 16, Issue 1s

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Project of National Engineering Laboratory for Internet Medical System and Application
National Natural Science Foundation of China

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Cited By

Han HJiang BShi YJiang PZhang HShang CSun YLi YXiang D(2023)Response of the Northwest Indian Ocean purpleback flying squid (Sthenoteuthis oualaniensis) fishing grounds to marine environmental changes and its prediction model construction based on multi-models and multi-spatial and temporal scalesEcological Indicators10.1016/j.ecolind.2023.110809154(110809)Online publication date: Oct-2023
https://doi.org/10.1016/j.ecolind.2023.110809
Jenkin Suji RBhadauria SWilfred Godfrey W(2023)A survey and taxonomy of 2.5D approaches for lung segmentation and nodule detection in CT imagesComputers in Biology and Medicine10.1016/j.compbiomed.2023.107437165(107437)Online publication date: Oct-2023
https://doi.org/10.1016/j.compbiomed.2023.107437
Nguyen CNguyen LTran G(2022)DATA-CENTRIC DEEP LEARNING METHOD FOR PULMONARY NODULE DETECTIONJournal of Computer Science and Cybernetics10.15625/1813-9663/38/3/1722038:3(229-243)Online publication date: 22-Sep-2022
https://doi.org/10.15625/1813-9663/38/3/17220
Chen YJhong SHsia C(2022)Roadside Unit-based Unknown Object Detection in Adverse Weather Conditions for Smart Internet of VehiclesACM Transactions on Management Information Systems10.1145/355492313:4(1-21)Online publication date: 4-Nov-2022
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Zhen PWang SZhang SYan XWang WJi ZChen H(2022)Towards Accurate Oriented Object Detection in Aerial Images with Adaptive Multi-level Feature FusionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/351313319:1(1-22)Online publication date: 18-Feb-2022
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Li ZYu X(2022)Power Load Curve Clustering based on ISODATA2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)10.1109/SmartCloud55982.2022.00022(104-108)Online publication date: Oct-2022
https://doi.org/10.1109/SmartCloud55982.2022.00022
Hou S(2022)Auxiliary tumour diagnosis image with deep learning technologyThe Journal of Supercomputing10.1007/s11227-021-03881-778:1(578-595)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11227-021-03881-7
Charoenphakdee NVongkulbhisal JChairatanakul NSugiyama M(2021)On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00516(5198-5207)Online publication date: Jun-2021
https://doi.org/10.1109/CVPR46437.2021.00516
Nguyen CTran GNguyen VBurie JNghiem T(2021)Pulmonary Nodule Detection Based on Faster R-CNN With Adaptive Anchor BoxIEEE Access10.1109/ACCESS.2021.31289429(154740-154751)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3128942

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