To segment or not to segment: COVID-19 detection for chest X-rays
Artificial intelligence (AI) has been integrated into most technologies we use. One of the most promising applications in AI is medical imaging. Research demonstrates that AI has improved the performance of most medical imaging analysis systems. Consequently, AI has become a fundamental element of t...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914823001247 |
| _version_ | 1797785507073097728 |
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| author | Sara Al Hajj Ibrahim Khalil El-Khatib |
| author_facet | Sara Al Hajj Ibrahim Khalil El-Khatib |
| author_sort | Sara Al Hajj Ibrahim |
| collection | DOAJ |
| description | Artificial intelligence (AI) has been integrated into most technologies we use. One of the most promising applications in AI is medical imaging. Research demonstrates that AI has improved the performance of most medical imaging analysis systems. Consequently, AI has become a fundamental element of the state of the art with improved outcomes across a variety of medical imaging applications. Moreover, it is believed that computer vision (CV) algorithms are highly effective for image analysis. Recent advances in CV facilitate the recognition of patterns in medical images. In this manner, we investigate CV segmentation techniques for COVID-19 analysis. We use different segmentation techniques, such as k-means, U-net, and flood fill, to extract the lung region from CXRs. Afterwards, we compare the effectiveness of these three segmentation approaches when applied to CXRs. Then, we use machine learning (ML) and deep learning (DL) models to identify COVID-19 lesion molecules in both healthy and pathological lung x-rays. We evaluate our ML and DL findings in the context of CV techniques. Our results indicate that the segmentation-related CV techniques do not exhibit comparable performance to DL and ML techniques. The most optimal AI algorithm yields an accuracy range of 0.92–0.94, whereas the addition of CV algorithms leads to a reduction in accuracy to approximately the range of 0.81–0.88. In addition, we test the performance of DL models under real-world noise, such as salt and pepper noise, which negatively impacts the overall performance. |
| first_indexed | 2024-03-13T00:55:02Z |
| format | Article |
| id | doaj.art-452348542bd34357b0260aae6fda25a4 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-03-13T00:55:02Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-452348542bd34357b0260aae6fda25a42023-07-07T04:27:22ZengElsevierInformatics in Medicine Unlocked2352-91482023-01-0140101280To segment or not to segment: COVID-19 detection for chest X-raysSara Al Hajj Ibrahim0Khalil El-Khatib1Corresponding author.; Ontario Tech University, CanadaOntario Tech University, CanadaArtificial intelligence (AI) has been integrated into most technologies we use. One of the most promising applications in AI is medical imaging. Research demonstrates that AI has improved the performance of most medical imaging analysis systems. Consequently, AI has become a fundamental element of the state of the art with improved outcomes across a variety of medical imaging applications. Moreover, it is believed that computer vision (CV) algorithms are highly effective for image analysis. Recent advances in CV facilitate the recognition of patterns in medical images. In this manner, we investigate CV segmentation techniques for COVID-19 analysis. We use different segmentation techniques, such as k-means, U-net, and flood fill, to extract the lung region from CXRs. Afterwards, we compare the effectiveness of these three segmentation approaches when applied to CXRs. Then, we use machine learning (ML) and deep learning (DL) models to identify COVID-19 lesion molecules in both healthy and pathological lung x-rays. We evaluate our ML and DL findings in the context of CV techniques. Our results indicate that the segmentation-related CV techniques do not exhibit comparable performance to DL and ML techniques. The most optimal AI algorithm yields an accuracy range of 0.92–0.94, whereas the addition of CV algorithms leads to a reduction in accuracy to approximately the range of 0.81–0.88. In addition, we test the performance of DL models under real-world noise, such as salt and pepper noise, which negatively impacts the overall performance.http://www.sciencedirect.com/science/article/pii/S2352914823001247Artificial intelligenceMachine learningComputer visionImage processingCOVID-19 detection |
| spellingShingle | Sara Al Hajj Ibrahim Khalil El-Khatib To segment or not to segment: COVID-19 detection for chest X-rays Informatics in Medicine Unlocked Artificial intelligence Machine learning Computer vision Image processing COVID-19 detection |
| title | To segment or not to segment: COVID-19 detection for chest X-rays |
| title_full | To segment or not to segment: COVID-19 detection for chest X-rays |
| title_fullStr | To segment or not to segment: COVID-19 detection for chest X-rays |
| title_full_unstemmed | To segment or not to segment: COVID-19 detection for chest X-rays |
| title_short | To segment or not to segment: COVID-19 detection for chest X-rays |
| title_sort | to segment or not to segment covid 19 detection for chest x rays |
| topic | Artificial intelligence Machine learning Computer vision Image processing COVID-19 detection |
| url | http://www.sciencedirect.com/science/article/pii/S2352914823001247 |
| work_keys_str_mv | AT saraalhajjibrahim tosegmentornottosegmentcovid19detectionforchestxrays AT khalilelkhatib tosegmentornottosegmentcovid19detectionforchestxrays |