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Quality Evaluation Method for Chest X-Ray Images using the Reference Patterns
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Publication Year
2022-01-01
Journal
Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022, pp.266-269
Keyword
Chest X-rayDeep LearningMachine LearningQuality Evaluation
Mesh Keyword
Chest X-rayChest X-ray imageDeep learningDiagnostic modelHigh quality dataMachine-learningModeling performanceQuality evaluationQuality evaluation methodReference patterns
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Science ApplicationsComputer Vision and Pattern RecognitionHardware and ArchitectureHuman-Computer InteractionElectrical and Electronic Engineering
Abstract
Chest X-Ray image quality evaluation relies on clinicians' judgment with clinical guidelines and expertise. Recently, the deep learning model for disease diagnosis has been made using X-ray image datasets. In this case, the quality of the image, which is the training model's input, determines the diagnostic model's performance. However, there are cost and time limitations to relying solely on the evaluation of clinicians for the massive amount of images. Therefore, there is a need to automatically evaluate the checklist of a given guideline and select a high-quality image. This paper generates a high-quality pattern from the chest X-ray image, and the difference between pixels from the target image is imaged. It is a method of evaluating quality with a CNN-based model by patterning the pixel value distribution of the generated image. The proposed method shows that it is possible to evaluate multiple integrated items, not a single evaluation item. In addition, to confirm the effectiveness of the proposed model, data composed by refining only randomly configured data and high-quality data without applying quality evaluation were applied to the Nodule Diagnostics Model. As a result of the experiment, model performance improved by 19% accuracy and 21% AUC when training with high-quality data.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36776
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85139037581&origin=inward
DOI
https://doi.org/10.1109/aicas54282.2022.9870023
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9869844
Type
Conference
Funding
Acknowledgements \This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2022-2020-0-01461) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)\
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LEE, JUNG WON Image
LEE, JUNG WON이정원
Department of Electrical and Computer Engineering
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