Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images
Tuberculosis (TB) is a disease that causes death if not treated early. Ensemble deep learning can aid early TB detection. Previous work trained the ensemble classifiers on images with similar features only. An ensemble requires a diversity of errors to perform well, which is achieved using either di...
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Online Access: | https://eprints.ums.edu.my/id/eprint/26737/1/Tuberculosis%20detection%20using%20deep%20learning%20and%20contrastenhanced%20canny%20edge%20detected%20X-Ray%20images1.pdf https://eprints.ums.edu.my/id/eprint/26737/2/Tuberculosis%20detection%20using%20deep%20learning%20and%20contrastenhanced%20canny%20edge%20detected%20X-Ray%20images.pdf https://eprints.ums.edu.my/id/eprint/26737/ http://doi.org/10.11591/ijai.v9.i4.pp713-720 |
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my.ums.eprints.267372021-04-14T01:05:32Z https://eprints.ums.edu.my/id/eprint/26737/ Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images Kieu, Stefanus Tao Hwa Abdullah Bade Mohd Hanafi Ahmad Hijazi Mohammad Saffree Jeffree Q Science (General) T Technology (General) Tuberculosis (TB) is a disease that causes death if not treated early. Ensemble deep learning can aid early TB detection. Previous work trained the ensemble classifiers on images with similar features only. An ensemble requires a diversity of errors to perform well, which is achieved using either different classification techniques or feature sets. This paper focuses on the latter, where TB detection using deep learning and contrast-enhanced canny edge detected (CEED-Canny) x-ray images is presented. The CEED-Canny was utilized to produce edge detected images of the lung x-ray. Two types of features were generated; the first was extracted from the Enhanced x-ray images, while the second from the Edge detected images. The proposed variation of features increased the diversity of errors of the base classifiers and improved the TB detection. The proposed ensemble method produced a comparable accuracy of 93.59%, sensitivity of 92.31% and specificity of 94.87% with previous work. 2020 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/26737/1/Tuberculosis%20detection%20using%20deep%20learning%20and%20contrastenhanced%20canny%20edge%20detected%20X-Ray%20images1.pdf text en https://eprints.ums.edu.my/id/eprint/26737/2/Tuberculosis%20detection%20using%20deep%20learning%20and%20contrastenhanced%20canny%20edge%20detected%20X-Ray%20images.pdf Kieu, Stefanus Tao Hwa and Abdullah Bade and Mohd Hanafi Ahmad Hijazi and Mohammad Saffree Jeffree (2020) Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images. IAES International Journal of Artificial Intelligence (IJ-AI), 9 (4). pp. 713-720. ISSN 2252-8938 http://doi.org/10.11591/ijai.v9.i4.pp713-720 |
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Q Science (General) T Technology (General) Kieu, Stefanus Tao Hwa Abdullah Bade Mohd Hanafi Ahmad Hijazi Mohammad Saffree Jeffree Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images |
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Tuberculosis (TB) is a disease that causes death if not treated early. Ensemble deep learning can aid early TB detection. Previous work trained the ensemble classifiers on images with similar features only. An ensemble requires a diversity of errors to perform well, which is achieved using either different classification techniques or feature sets. This paper focuses on the latter, where TB detection using deep learning and contrast-enhanced canny edge detected (CEED-Canny) x-ray images is presented. The CEED-Canny was utilized to produce edge detected images of the lung x-ray. Two types of features were generated; the first was extracted from the Enhanced x-ray images, while the second from the Edge detected images. The proposed variation of features increased the diversity of errors of the base classifiers and improved the TB detection. The proposed ensemble method produced a comparable accuracy of 93.59%, sensitivity of 92.31% and specificity of 94.87% with previous work. |
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Article |
author |
Kieu, Stefanus Tao Hwa Abdullah Bade Mohd Hanafi Ahmad Hijazi Mohammad Saffree Jeffree |
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Kieu, Stefanus Tao Hwa Abdullah Bade Mohd Hanafi Ahmad Hijazi Mohammad Saffree Jeffree |
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Kieu, Stefanus Tao Hwa |
title |
Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images |
title_short |
Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images |
title_full |
Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images |
title_fullStr |
Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images |
title_full_unstemmed |
Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images |
title_sort |
tuberculosis detection using deep learning and contrastenhanced canny edge detected x-ray images |
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2020 |
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https://eprints.ums.edu.my/id/eprint/26737/1/Tuberculosis%20detection%20using%20deep%20learning%20and%20contrastenhanced%20canny%20edge%20detected%20X-Ray%20images1.pdf https://eprints.ums.edu.my/id/eprint/26737/2/Tuberculosis%20detection%20using%20deep%20learning%20and%20contrastenhanced%20canny%20edge%20detected%20X-Ray%20images.pdf https://eprints.ums.edu.my/id/eprint/26737/ http://doi.org/10.11591/ijai.v9.i4.pp713-720 |
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