TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique
Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed sin...
Ausführliche Beschreibung
Autor*in: |
Syed Thouheed Ahmed [verfasserIn] Syed Muzamil Basha [verfasserIn] Muthukumaran Venkatesan [verfasserIn] Sandeep Kumar Mathivanan [verfasserIn] Saurav Mallik [verfasserIn] Najah Alsubaie [verfasserIn] Mohammed S. Alqahtani [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
In: BMC Medical Imaging - BMC, 2003, 23(2023), 1, Seite 10 |
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Übergeordnetes Werk: |
volume:23 ; year:2023 ; number:1 ; pages:10 |
Links: |
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DOI / URN: |
10.1186/s12880-023-01100-8 |
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Katalog-ID: |
DOAJ09188151X |
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10.1186/s12880-023-01100-8 doi (DE-627)DOAJ09188151X (DE-599)DOAJ522d68a52a684b0881f63cd9c36cc9ac DE-627 ger DE-627 rakwb eng R855-855.5 Syed Thouheed Ahmed verfasserin aut TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets. COVID-19 X-Ray image classification Feature-alignment Corona virus classification COVID-19 detection Medical technology Syed Muzamil Basha verfasserin aut Muthukumaran Venkatesan verfasserin aut Sandeep Kumar Mathivanan verfasserin aut Saurav Mallik verfasserin aut Najah Alsubaie verfasserin aut Mohammed S. Alqahtani verfasserin aut In BMC Medical Imaging BMC, 2003 23(2023), 1, Seite 10 (DE-627)33679911X (DE-600)2061975-3 14712342 nnns volume:23 year:2023 number:1 pages:10 https://doi.org/10.1186/s12880-023-01100-8 kostenfrei https://doaj.org/article/522d68a52a684b0881f63cd9c36cc9ac kostenfrei https://doi.org/10.1186/s12880-023-01100-8 kostenfrei https://doaj.org/toc/1471-2342 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 10 |
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10.1186/s12880-023-01100-8 doi (DE-627)DOAJ09188151X (DE-599)DOAJ522d68a52a684b0881f63cd9c36cc9ac DE-627 ger DE-627 rakwb eng R855-855.5 Syed Thouheed Ahmed verfasserin aut TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets. COVID-19 X-Ray image classification Feature-alignment Corona virus classification COVID-19 detection Medical technology Syed Muzamil Basha verfasserin aut Muthukumaran Venkatesan verfasserin aut Sandeep Kumar Mathivanan verfasserin aut Saurav Mallik verfasserin aut Najah Alsubaie verfasserin aut Mohammed S. Alqahtani verfasserin aut In BMC Medical Imaging BMC, 2003 23(2023), 1, Seite 10 (DE-627)33679911X (DE-600)2061975-3 14712342 nnns volume:23 year:2023 number:1 pages:10 https://doi.org/10.1186/s12880-023-01100-8 kostenfrei https://doaj.org/article/522d68a52a684b0881f63cd9c36cc9ac kostenfrei https://doi.org/10.1186/s12880-023-01100-8 kostenfrei https://doaj.org/toc/1471-2342 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 10 |
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10.1186/s12880-023-01100-8 doi (DE-627)DOAJ09188151X (DE-599)DOAJ522d68a52a684b0881f63cd9c36cc9ac DE-627 ger DE-627 rakwb eng R855-855.5 Syed Thouheed Ahmed verfasserin aut TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets. COVID-19 X-Ray image classification Feature-alignment Corona virus classification COVID-19 detection Medical technology Syed Muzamil Basha verfasserin aut Muthukumaran Venkatesan verfasserin aut Sandeep Kumar Mathivanan verfasserin aut Saurav Mallik verfasserin aut Najah Alsubaie verfasserin aut Mohammed S. Alqahtani verfasserin aut In BMC Medical Imaging BMC, 2003 23(2023), 1, Seite 10 (DE-627)33679911X (DE-600)2061975-3 14712342 nnns volume:23 year:2023 number:1 pages:10 https://doi.org/10.1186/s12880-023-01100-8 kostenfrei https://doaj.org/article/522d68a52a684b0881f63cd9c36cc9ac kostenfrei https://doi.org/10.1186/s12880-023-01100-8 kostenfrei https://doaj.org/toc/1471-2342 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 10 |
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Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets. |
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Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets. |
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Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets. |
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The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. 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