Details der Publikation - Detection of COVID-19 from X-rays using hybrid deep learning models

Detection of COVID-19 from X-rays using hybrid deep learning models

Purpose To propose a model that can detect the presence of Covid-19 from chest X-rays and can be used with low hardware resource-based personal digital assistants (PDA). Methods In this paper, a hybrid deep learning model is proposed for the detection of coronavirus from chest X-ray images. The hybrid deep learning model is a combination of ResNet50 and MobileNet. Both ResNet50 and MobileNet are light deep neural networks (DNNs) and can be used with low hardware resource-based personal digital assistants (PDA) for quick detection of COVID-19 infection. Results The performance of the proposed hybrid model is evaluated on two publicly available COVID-19 chest X-ray datasets. Both datasets include normal, pneumonia, and coronavirus-infected chest X-rays and we achieve 84.35% and 94.43% accuracy on Dataset 1 and Dataset 2 respectively. Conclusion Results show that the proposed hybrid model is better suited for COVID-19 detection..

Medienart:	Artikel

Erscheinungsjahr:	2021
Erschienen:	2021

Enthalten in:	Zur Gesamtaufnahme - volume:37
Enthalten in:	Research on biomedical engineering - 37(2021), 4 vom: 21. Sept., Seite 687-695

Sprache:	Englisch

Beteiligte Personen:	Nandi, Ritika [VerfasserIn] Mulimani, Manjunath [VerfasserIn]

Links:	Volltext [lizenzpflichtig]

Themen:	COVID-19 detection Hybrid model MobileNet Pneumonia ResNet50 X-rays

Anmerkungen:	© Sociedade Brasileira de Engenharia Biomedica 2021

doi:	10.1007/s42600-021-00181-0

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	OLC2077575034

Internformat


LEADER	01000caa a22002652 4500
001	OLC2077575034
003	DE-627
005	20230511182307.0
007	tu
008	221220s2021 xx \|\|\|\|\| 00\| \|\|eng c
024	7		\|a 10.1007/s42600-021-00181-0 \|2 doi
035			\|a (DE-627)OLC2077575034
035			\|a (DE-He213)s42600-021-00181-0-p
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 610 \|q VZ
082	0	4	\|a 570 \|a 610 \|a 620 \|q VZ
100	1		\|a Nandi, Ritika \|e verfasserin \|0 (orcid)0000-0002-4782-0642 \|4 aut
245	1	0	\|a Detection of COVID-19 from X-rays using hybrid deep learning models
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a ohne Hilfsmittel zu benutzen \|b n \|2 rdamedia
338			\|a Band \|b nc \|2 rdacarrier
500			\|a © Sociedade Brasileira de Engenharia Biomedica 2021
520			\|a Purpose To propose a model that can detect the presence of Covid-19 from chest X-rays and can be used with low hardware resource-based personal digital assistants (PDA). Methods In this paper, a hybrid deep learning model is proposed for the detection of coronavirus from chest X-ray images. The hybrid deep learning model is a combination of ResNet50 and MobileNet. Both ResNet50 and MobileNet are light deep neural networks (DNNs) and can be used with low hardware resource-based personal digital assistants (PDA) for quick detection of COVID-19 infection. Results The performance of the proposed hybrid model is evaluated on two publicly available COVID-19 chest X-ray datasets. Both datasets include normal, pneumonia, and coronavirus-infected chest X-rays and we achieve 84.35% and 94.43% accuracy on Dataset 1 and Dataset 2 respectively. Conclusion Results show that the proposed hybrid model is better suited for COVID-19 detection.
650		4	\|a COVID-19 detection
650		4	\|a MobileNet
650		4	\|a ResNet50
650		4	\|a Hybrid model
650		4	\|a Pneumonia
650		4	\|a X-rays
700	1		\|a Mulimani, Manjunath \|4 aut
773	0	8	\|i Enthalten in \|t Research on biomedical engineering \|d Springer International Publishing, 2015 \|g 37(2021), 4 vom: 21. Sept., Seite 687-695 \|h Online-Ressource \|w (DE-627)890513783 \|w (DE-600)2897414-1 \|w (DE-576)48986211X \|x 2446-4740 \|7 nnns
773	1	8	\|g volume:37 \|g year:2021 \|g number:4 \|g day:21 \|g month:09 \|g pages:687-695
856	4	1	\|u https://doi.org/10.1007/s42600-021-00181-0 \|z lizenzpflichtig \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_OLC
912			\|a SSG-OLC-PHA
912			\|a SSG-OLC-DE-84
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_31
912			\|a GBV_ILN_32
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_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2132
912			\|a GBV_ILN_2136
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2474
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
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_4326
912			\|a GBV_ILN_4328
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 37 \|j 2021 \|e 4 \|b 21 \|c 09 \|h 687-695

Detection of COVID-19 from X-rays using hybrid deep learning models

Zugang & Verfügbarkeit

Zugehörige Publikationen/Bände