research-article

Performance Comparison of Deep Learning Models for Black Lung Detection on Chest X-ray Radiographs

Authors:

Dadong WangAuthors Info & Claims

ICSIM '20: Proceedings of the 3rd International Conference on Software Engineering and Information Management

Pages 150 - 154

https://doi.org/10.1145/3378936.3378968

Published: 07 March 2020 Publication History

Abstract

Black Lung (BL) is an incurable respiratory disease caused by long term inhalation of respirable coal dust. Confidentiality restrictions and disease incidence limit the availability of BL datasets, which presents significant challenges in the training of deep learning (DL) models. This paper presents the implementations and detailed performance comparison of seven DL models for BL detection with small datasets. The models include VGG16, VGG19, InceptionV3, Xception, ResNet50, DenseNet121 and CheXNet. A small BL dataset of real and synthetic images was used to train the seven deep learning models. Segmented lung X-ray images, with and without BL, were used as training images to establish a benchmark. To increase the number of images required for training a deep learning system the training data set was augmented, using a Cycle-Consistent Adversarial Networks (CycleGAN) and the Keras Image Data Generator, to generate additional augmented and synthetic radiographs. The effects of different dropout nodes as a blocking factor was also investigated on all seven models. The best sensitivity (Normal Prediction Rate), specificity (BL prediction Rate), error rate (ERR or incorrect prediction rate), accuracy (1-ERR), as well as total execution time for binary classification for each model, with and without augmentation, was compared for optimal BL detection. On average, the CheXNet model gave the best performance of all seven DL models.

References

[1]

G. R Zosky, et al. 2016. Coal workers' pneumoconiosis: an Australian perspective. The Medical Journal of Australia. 204(11), (20 June 2016), 414--418.

[2]

R. F Hoy and F. Brims, 2017. Occupational lung diseases in Australia. The Medcal Journal of Australia. 207(10), (13 November 2017), 443--448.

[3]

J. F. Colinet, et al. 2010. Best Practices for Dust Control in Coal Mining. National Institute for Occupational Safety and Health.https://www.cdc.gov/niosh/mining/UserFiles/works/pdfs/2010-110.pdf

[4]

Inquiry into the re-identification of Coal Workers' Pneumoconiosis in Queensland, Report No. 2, 55th Parliament Coal Workers' Pneumoconiosis Select Committee, 2017. https://www.parliament.qld.gov.au/documents/tableOffice/TabledPapers/2017/5517T815.pdf

[5]

A. Rehman, et al. 2018. Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique, 81(11), (23 October 2018).

[6]

A. Esteva, et al. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), (2 February 2017), 115--118. 10.1038/nature21056

[7]

V. Gulshan, et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 316(22), (13 Dec 2016), 2402--2410. 10.1001/jama.2016.17216

[8]

G. S. Tandel, et al. 2019. A Review on a Deep Learning Perspective in Brain Cancer Classification. Cancers, 11(1), (18 Jan 2019), 111.

[9]

P. Rajpurkar, et al. 2017. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. (2017).

[10]

R. V. M. da Nóbrega, et al. 2018. Lung Nodule Classification via Deep Transfer Learning in CT Lung Images. In 2018 IEEE 31st ISCBMS, IEEE. (2018), 244--249.

[11]

Y. Yu et al. 2017. Deep Transfer Learning for Modality Classification of Medical Images. Information. 8(3), (29 July 2017), 91.

[12]

S. Singh, et al. 2018. Modality Classification and Concept Detection in Medical Images using Deep Transfer Learning. In Proc. of IVCNZ, IEEE. (2018), 1--9.

[13]

W. Ausawalaithong, et al. 2018. Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach. In Proc. of BMEiCON. (2018), 1--5.

[14]

K. Simonyan and A. Zisserman. 2015. Very deep convolutional networks for large-scale image recognition, In Proc. of CVPR, IEEE. (10 Apr 2015), 1--14.

[15]

C. Szegedy, et al. 2014. Going Deeper with Convolutions. In Proc. of CVPR, IEEE. (17 Sep 2014), 1--9.

[16]

François Chollet. 2017. Xception: Deep Learning with Depthwise Separable Convolutions. In Proc. of CVPR, IEEE. (4 Apr 2017), 1--8.

[17]

K. He, et al. 2015. Deep Residual Learning for Image Recognition. In Proc. of CVPR, IEEE. (10 Dec 2015), 1--12.

[18]

G. Huang, et al. 2018. Densely Connected Convolutional Networks. In Proceedings of CVPR, IEEE, (28 Jan 2018), 4700--4708.

[19]

J. Zhu, et al. 2017. Efros. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In Proc. of CVPR. (15 Nov 2017), 2223--2232.

[20]

M. Nakamura, K. Abe and M. Minami. 2009. Quantitative evaluation of pneumoconiosis in chest radiographs obtained with a CCD scanner. In Proc. of ICADIWT, IEEE. (02 October 2009), 646--651.

[21]

M. Nakamura, K. Abe and M. Minami. 2010. Extraction of features for diagnosing pneumoconiosis from chest radiographs obtained with a CCD scanner. JDIM. 8(3), (2010) 147--152.

[22]

K. Abe1, et al. 2013. Computer-aided diagnosis of pneumoconiosis X-ray images scanned with a common CCD scanner. Automation Control and Intelligent Systems. 1(2), (02 April 2013), 24--33.

[23]

P. Yu, et al. 2009. Computer Aided Detection for Pneumoconiosis Based on Histogram Analysis. In Proc. of ICISE, IEEE. (26 April 2010), 3625--3628.

Digital Library

[24]

P. Yu, et al. Computer Aided Detection for Pneumoconiosis Based on Co-occurrence Matrices Analysis. In Proc. of ICBEI, IEEE. (30 Oct 2009), 1--4.

[25]

P. Yu, et al. 2011. An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs. J of Digital Imaging. 24(3), (June 2011), 382--393.

[26]

R. Sundararajan, et al. 2010. A Multiresolution Support Vector Machine Based Algorithm for Pneumoconiosis Detection from Chest X-ray Radiographs. In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE. (21 June 2010), 1317--1320.

[27]

H. Xu, et al. 2010. Computer Aided Detection for Pneumoconiosis Screening on Digital Chest Radiographs. In Proceedings of the Third International Workshop on Pulmonary Image Analysis. (2010), 129--138.

[28]

O. Russakovsky, et al., 2015. Imagenet large scale visual recognition challenge. International journal of CVPR. 115(3), (30 Jan 2015), 211--252.

Cited By

Liu MLoveless IHuang ZRosenman KWang LAlessio A(2024)Deep learning methods for multi-class pneumoconioses grading of chest radiographsMedical Imaging 2024: Computer-Aided Diagnosis10.1117/12.3006266(97)Online publication date: 3-Apr-2024
https://doi.org/10.1117/12.3006266
Shah IMishra S(2024)Artificial intelligence in advancing occupational health and safety: an encapsulation of developmentsJournal of Occupational Health10.1093/joccuh/uiad01766:1Online publication date: 3-Jan-2024
https://doi.org/10.1093/joccuh/uiad017
Akbar KKallawicha K(2024)Black lung disease among coal miners in Asia: a systematic reviewSafety and Health at Work10.1016/j.shaw.2024.01.005Online publication date: Feb-2024
https://doi.org/10.1016/j.shaw.2024.01.005
Show More Cited By

Index Terms

Performance Comparison of Deep Learning Models for Black Lung Detection on Chest X-ray Radiographs
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs
Abstract
Early detection of pneumoconiosis in X-Rays has been a challenging task that leads to high inter- and intra-reader variability. Motivated by the success of deep learning in general and medical image classification, this paper proposes an approach ...
Highlights
- A multilevel deep feature-based classifier is proposed to automatically detect pneumoconiosis in chest x-ray radiographs.
- Transfer learning of CheXNet is employed to extract different dimensional of low and high-level features from X-...
An Improved CNN-Based Pneumoconiosis Diagnosis Method on X-ray Chest Film
Human Centered Computing
Abstract
Pneumoconiosis is one of the most serious occupational diseases in China, which seriously endangers the health of most workers in dust environments. The diagnosis of pneumoconiosis is very complex and cumbersome, which relies mostly on doctor’s ...
Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning
ICFNDS '23: Proceedings of the 7th International Conference on Future Networks and Distributed Systems

Tuberculosis (TB) is a deadly and widespread lung disease that is often not easily detectable in the early stages. Thanks to the availability of high-resolution chest X-rays, deep learning (DL) is now able to help with the successful detection of this ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICSIM '20: Proceedings of the 3rd International Conference on Software Engineering and Information Management

January 2020

258 pages

ISBN:9781450376907

DOI:10.1145/3378936

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]

In-Cooperation

University of Science and Technology of China: University of Science and Technology of China

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICSIM '20

ICSIM '20: The 3rd International Conference on Software Engineering and Information Management

January 12 - 15, 2020

NSW, Sydney, Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
209
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)2

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu MLoveless IHuang ZRosenman KWang LAlessio A(2024)Deep learning methods for multi-class pneumoconioses grading of chest radiographsMedical Imaging 2024: Computer-Aided Diagnosis10.1117/12.3006266(97)Online publication date: 3-Apr-2024
https://doi.org/10.1117/12.3006266
Shah IMishra S(2024)Artificial intelligence in advancing occupational health and safety: an encapsulation of developmentsJournal of Occupational Health10.1093/joccuh/uiad01766:1Online publication date: 3-Jan-2024
https://doi.org/10.1093/joccuh/uiad017
Akbar KKallawicha K(2024)Black lung disease among coal miners in Asia: a systematic reviewSafety and Health at Work10.1016/j.shaw.2024.01.005Online publication date: Feb-2024
https://doi.org/10.1016/j.shaw.2024.01.005
Alam MWang DSowmya A(2024)AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray imagesArtificial Intelligence in Medicine10.1016/j.artmed.2024.102917154(102917)Online publication date: Aug-2024
https://doi.org/10.1016/j.artmed.2024.102917
Akbar KChao HThanvisitthpon NWongsasuluk PKallawicha K(2024)Respiratory diseases caused by air pollutantsDiseases and Health Consequences of Air Pollution10.1016/B978-0-443-16080-6.00005-7(27-53)Online publication date: 2024
https://doi.org/10.1016/B978-0-443-16080-6.00005-7
Shwetha SRamana N(2024)Analyze and Detect Lung Disorders Using Machine Learning Approaches—A Systematic ReviewComputational Intelligence in Machine Learning10.1007/978-981-99-7954-7_22(237-246)Online publication date: 21-Feb-2024
https://doi.org/10.1007/978-981-99-7954-7_22
Parsarad SSaeedizadeh NSoufi GShafieyoon SHekmatnia FZarei ASoleimany SYousefi ANazari HTorabi PS. Milani AMadani Tonekaboni SRabbani HHekmatnia AKafieh R(2023)Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT DatasetJournal of Imaging10.3390/jimaging90801599:8(159)Online publication date: 8-Aug-2023
https://doi.org/10.3390/jimaging9080159
Karamti HAlharthi RAnizi AAlhebshi REshmawi AAlsubai SUmer M(2023)Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning ApproachCancers10.3390/cancers1517441215:17(4412)Online publication date: 4-Sep-2023
https://doi.org/10.3390/cancers15174412
Bendouch MFrasincar FRobal T(2023)A visual-semantic approach for building content-based recommender systemsInformation Systems10.1016/j.is.2023.102243117:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102243
Wang YCui FDing XYao YLi GGui GShen FLi B(2023)Automated identification of the preclinical stage of coal workers' pneumoconiosis from digital chest radiography using three-stage cascaded deep learning modelBiomedical Signal Processing and Control10.1016/j.bspc.2023.10460783(104607)Online publication date: May-2023
https://doi.org/10.1016/j.bspc.2023.104607
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents