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Computer-aided detection in chest radiography based on artificial intelligence: a survey

Overview of attention for article published in BioMedical Engineering OnLine, August 2018
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Mentioned by

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2 tweeters

Citations

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183 Dimensions

Readers on

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343 Mendeley
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Title
Computer-aided detection in chest radiography based on artificial intelligence: a survey
Published in
BioMedical Engineering OnLine, August 2018
DOI 10.1186/s12938-018-0544-y
Pubmed ID
Authors

Chunli Qin, Demin Yao, Yonghong Shi, Zhijian Song

Abstract

As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 343 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 343 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 59 17%
Student > Master 57 17%
Student > Ph. D. Student 37 11%
Researcher 24 7%
Student > Postgraduate 15 4%
Other 57 17%
Unknown 94 27%
Readers by discipline Count As %
Computer Science 82 24%
Engineering 44 13%
Medicine and Dentistry 43 13%
Nursing and Health Professions 25 7%
Physics and Astronomy 8 2%
Other 34 10%
Unknown 107 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 January 2021.
All research outputs
#15,440,627
of 19,957,201 outputs
Outputs from BioMedical Engineering OnLine
#478
of 766 outputs
Outputs of similar age
#206,520
of 293,013 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 1 outputs
Altmetric has tracked 19,957,201 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 766 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 293,013 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them