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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 X users

Citations

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250 Dimensions

Readers on

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413 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.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 413 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 413 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 66 16%
Student > Master 62 15%
Student > Ph. D. Student 41 10%
Researcher 32 8%
Lecturer 18 4%
Other 59 14%
Unknown 135 33%
Readers by discipline Count As %
Computer Science 92 22%
Engineering 53 13%
Medicine and Dentistry 50 12%
Nursing and Health Professions 29 7%
Biochemistry, Genetics and Molecular Biology 8 2%
Other 31 8%
Unknown 150 36%
Attention Score in Context

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
#17,987,988
of 23,102,082 outputs
Outputs from BioMedical Engineering OnLine
#532
of 825 outputs
Outputs of similar age
#239,834
of 334,082 outputs
Outputs of similar age from BioMedical Engineering OnLine
#9
of 18 outputs
Altmetric has tracked 23,102,082 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 825 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 31st percentile – i.e., 31% 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 334,082 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.