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

Gespeichert in:

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]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	COVID-19 X-Ray image classification Feature-alignment Corona virus classification COVID-19 detection

Übergeordnetes Werk:	In: BMC Medical Imaging - BMC, 2003, 23(2023), 1, Seite 10
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:1 ; pages:10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12880-023-01100-8

Katalog-ID:	DOAJ09188151X

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ09188151X
003	DE-627
005	20240412215022.0
007	cr uuu---uuuuu
008	240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s12880-023-01100-8 \|2 doi
035			\|a (DE-627)DOAJ09188151X
035			\|a (DE-599)DOAJ522d68a52a684b0881f63cd9c36cc9ac
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a R855-855.5
100	0		\|a Syed Thouheed Ahmed \|e verfasserin \|4 aut
245	1	0	\|a TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a 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.
650		4	\|a COVID-19
650		4	\|a X-Ray image classification
650		4	\|a Feature-alignment
650		4	\|a Corona virus classification
650		4	\|a COVID-19 detection
653		0	\|a Medical technology
700	0		\|a Syed Muzamil Basha \|e verfasserin \|4 aut
700	0		\|a Muthukumaran Venkatesan \|e verfasserin \|4 aut
700	0		\|a Sandeep Kumar Mathivanan \|e verfasserin \|4 aut
700	0		\|a Saurav Mallik \|e verfasserin \|4 aut
700	0		\|a Najah Alsubaie \|e verfasserin \|4 aut
700	0		\|a Mohammed S. Alqahtani \|e verfasserin \|4 aut
773	0	8	\|i In \|t BMC Medical Imaging \|d BMC, 2003 \|g 23(2023), 1, Seite 10 \|w (DE-627)33679911X \|w (DE-600)2061975-3 \|x 14712342 \|7 nnns
773	1	8	\|g volume:23 \|g year:2023 \|g number:1 \|g pages:10
856	4	0	\|u https://doi.org/10.1186/s12880-023-01100-8 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/522d68a52a684b0881f63cd9c36cc9ac \|z kostenfrei
856	4	0	\|u https://doi.org/10.1186/s12880-023-01100-8 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1471-2342 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 23 \|j 2023 \|e 1 \|h 10

Indexfelder

author_variant	s t a sta s m b smb m v mv s k m skm s m sm n a na m s a msa
matchkey_str	article:14712342:2023----::vxoi1xaiaecasfctoapocuigerlewrsaef
hierarchy_sort_str	2023
callnumber-subject-code	R
publishDate	2023
allfields	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
spelling	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
allfields_unstemmed	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
allfieldsGer	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
allfieldsSound	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
language	English
source	In BMC Medical Imaging 23(2023), 1, Seite 10 volume:23 year:2023 number:1 pages:10
sourceStr	In BMC Medical Imaging 23(2023), 1, Seite 10 volume:23 year:2023 number:1 pages:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	COVID-19 X-Ray image classification Feature-alignment Corona virus classification COVID-19 detection Medical technology
isfreeaccess_bool	true
container_title	BMC Medical Imaging
authorswithroles_txt_mv	Syed Thouheed Ahmed @@aut@@ Syed Muzamil Basha @@aut@@ Muthukumaran Venkatesan @@aut@@ Sandeep Kumar Mathivanan @@aut@@ Saurav Mallik @@aut@@ Najah Alsubaie @@aut@@ Mohammed S. Alqahtani @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	33679911X
id	DOAJ09188151X
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ09188151X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412215022.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12880-023-01100-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ09188151X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ522d68a52a684b0881f63cd9c36cc9ac</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R855-855.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Syed Thouheed Ahmed</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COVID-19</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">X-Ray image classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature-alignment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Corona virus classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COVID-19 detection</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medical technology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Syed Muzamil Basha</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Muthukumaran Venkatesan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sandeep Kumar Mathivanan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Saurav Mallik</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Najah Alsubaie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammed S. Alqahtani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">BMC Medical Imaging</subfield><subfield code="d">BMC, 2003</subfield><subfield code="g">23(2023), 1, Seite 10</subfield><subfield code="w">(DE-627)33679911X</subfield><subfield code="w">(DE-600)2061975-3</subfield><subfield code="x">14712342</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:10</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s12880-023-01100-8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/522d68a52a684b0881f63cd9c36cc9ac</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s12880-023-01100-8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1471-2342</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="h">10</subfield></datafield></record></collection>
callnumber-first	R - Medicine
author	Syed Thouheed Ahmed
spellingShingle	Syed Thouheed Ahmed misc R855-855.5 misc COVID-19 misc X-Ray image classification misc Feature-alignment misc Corona virus classification misc COVID-19 detection misc Medical technology TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique
authorStr	Syed Thouheed Ahmed
ppnlink_with_tag_str_mv	@@773@@(DE-627)33679911X
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	R855-855
illustrated	Not Illustrated
issn	14712342
topic_title	R855-855.5 TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique COVID-19 X-Ray image classification Feature-alignment Corona virus classification COVID-19 detection
topic	misc R855-855.5 misc COVID-19 misc X-Ray image classification misc Feature-alignment misc Corona virus classification misc COVID-19 detection misc Medical technology
topic_unstemmed	misc R855-855.5 misc COVID-19 misc X-Ray image classification misc Feature-alignment misc Corona virus classification misc COVID-19 detection misc Medical technology
topic_browse	misc R855-855.5 misc COVID-19 misc X-Ray image classification misc Feature-alignment misc Corona virus classification misc COVID-19 detection misc Medical technology
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	BMC Medical Imaging
hierarchy_parent_id	33679911X
hierarchy_top_title	BMC Medical Imaging
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)33679911X (DE-600)2061975-3
title	TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique
ctrlnum	(DE-627)DOAJ09188151X (DE-599)DOAJ522d68a52a684b0881f63cd9c36cc9ac
title_full	TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique
author_sort	Syed Thouheed Ahmed
journal	BMC Medical Imaging
journalStr	BMC Medical Imaging
callnumber-first-code	R
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
container_start_page	10
author_browse	Syed Thouheed Ahmed Syed Muzamil Basha Muthukumaran Venkatesan Sandeep Kumar Mathivanan Saurav Mallik Najah Alsubaie Mohammed S. Alqahtani
container_volume	23
class	R855-855.5
format_se	Elektronische Aufsätze
author-letter	Syed Thouheed Ahmed
doi_str_mv	10.1186/s12880-023-01100-8
author2-role	verfasserin
title_sort	tvfx – covid-19 x-ray images classification approach using neural networks based feature thresholding technique
callnumber	R855-855.5
title_auth	TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
collection_details	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
container_issue	1
title_short	TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique
url	https://doi.org/10.1186/s12880-023-01100-8 https://doaj.org/article/522d68a52a684b0881f63cd9c36cc9ac https://doaj.org/toc/1471-2342
remote_bool	true
author2	Syed Muzamil Basha Muthukumaran Venkatesan Sandeep Kumar Mathivanan Saurav Mallik Najah Alsubaie Mohammed S. Alqahtani
author2Str	Syed Muzamil Basha Muthukumaran Venkatesan Sandeep Kumar Mathivanan Saurav Mallik Najah Alsubaie Mohammed S. Alqahtani
ppnlink	33679911X
callnumber-subject	R - General Medicine
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s12880-023-01100-8
callnumber-a	R855-855.5
up_date	2024-07-03T22:49:13.590Z
_version_	1803599954477842433
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ09188151X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412215022.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12880-023-01100-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ09188151X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ522d68a52a684b0881f63cd9c36cc9ac</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R855-855.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Syed Thouheed Ahmed</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COVID-19</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">X-Ray image classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature-alignment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Corona virus classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COVID-19 detection</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medical technology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Syed Muzamil Basha</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Muthukumaran Venkatesan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sandeep Kumar Mathivanan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Saurav Mallik</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Najah Alsubaie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammed S. Alqahtani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">BMC Medical Imaging</subfield><subfield code="d">BMC, 2003</subfield><subfield code="g">23(2023), 1, Seite 10</subfield><subfield code="w">(DE-627)33679911X</subfield><subfield code="w">(DE-600)2061975-3</subfield><subfield code="x">14712342</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:10</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s12880-023-01100-8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/522d68a52a684b0881f63cd9c36cc9ac</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s12880-023-01100-8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1471-2342</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="h">10</subfield></datafield></record></collection>
score	7.400614

Nicht das Richtige dabei?

Schreiben Sie uns!

TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?