Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309777854> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W4309777854 endingPage "200154" @default.
- W4309777854 startingPage "200154" @default.
- W4309777854 abstract "COVID-19 is an infectious disease that has cost millions of lives all over the world. A faster and safer diagnosis of COVID-19 is highly desirable in order to stop its spread. An electrocardiogram (ECG) signal-based diagnosis has shown its potential in the diagnosis of cardiac, stroke, and COVID-19. In this study, an ensemble of three deep learning models are used for COVID-19 detection in ECG images for multi-class classification. The results obtained with the weighted average ensemble technique have been improved by using the grid search technique. For multi-class classification, an optimized weighted average ensemble (OWAE) model classifies the ECG images with an accuracy of 95.29%, an F1-score of 95.4%, a precision of 95.5%, and a recall of 95.3%. In case of binary classification, VGG-19, EfficientNet-B4, and DenseNet-121 performed comparatively well with 100% accuracy. These results show that deep learning can be used in the diagnosis of COVID-19 disease using ECG images." @default.
- W4309777854 created "2022-11-29" @default.
- W4309777854 creator A5013568770 @default.
- W4309777854 creator A5055876182 @default.
- W4309777854 creator A5064109651 @default.
- W4309777854 creator A5064552136 @default.
- W4309777854 date "2022-11-01" @default.
- W4309777854 modified "2023-10-18" @default.
- W4309777854 title "OWAE-Net: COVID-19 detection from ECG images using deep learning and optimized weighted average ensemble technique" @default.
- W4309777854 cites W2010450719 @default.
- W4309777854 cites W2029941268 @default.
- W4309777854 cites W2949846539 @default.
- W4309777854 cites W2967602207 @default.
- W4309777854 cites W2988068973 @default.
- W4309777854 cites W3000039309 @default.
- W4309777854 cites W3004026249 @default.
- W4309777854 cites W3008028633 @default.
- W4309777854 cites W3012189167 @default.
- W4309777854 cites W3021718230 @default.
- W4309777854 cites W3087348791 @default.
- W4309777854 cites W3087597140 @default.
- W4309777854 cites W3088463325 @default.
- W4309777854 cites W3093512431 @default.
- W4309777854 cites W3093802981 @default.
- W4309777854 cites W3093948301 @default.
- W4309777854 cites W3124099916 @default.
- W4309777854 cites W3164746735 @default.
- W4309777854 cites W3165838540 @default.
- W4309777854 cites W3166195577 @default.
- W4309777854 cites W3167240651 @default.
- W4309777854 cites W3194080835 @default.
- W4309777854 cites W3206409898 @default.
- W4309777854 cites W4205267589 @default.
- W4309777854 cites W4213281705 @default.
- W4309777854 cites W4288081728 @default.
- W4309777854 doi "https://doi.org/10.1016/j.iswa.2022.200154" @default.
- W4309777854 hasPublicationYear "2022" @default.
- W4309777854 type Work @default.
- W4309777854 citedByCount "0" @default.
- W4309777854 crossrefType "journal-article" @default.
- W4309777854 hasAuthorship W4309777854A5013568770 @default.
- W4309777854 hasAuthorship W4309777854A5055876182 @default.
- W4309777854 hasAuthorship W4309777854A5064109651 @default.
- W4309777854 hasAuthorship W4309777854A5064552136 @default.
- W4309777854 hasBestOaLocation W43097778541 @default.
- W4309777854 hasConcept C108583219 @default.
- W4309777854 hasConcept C126322002 @default.
- W4309777854 hasConcept C153180895 @default.
- W4309777854 hasConcept C154945302 @default.
- W4309777854 hasConcept C2776654903 @default.
- W4309777854 hasConcept C2779134260 @default.
- W4309777854 hasConcept C3008058167 @default.
- W4309777854 hasConcept C38652104 @default.
- W4309777854 hasConcept C41008148 @default.
- W4309777854 hasConcept C45942800 @default.
- W4309777854 hasConcept C524204448 @default.
- W4309777854 hasConcept C71924100 @default.
- W4309777854 hasConceptScore W4309777854C108583219 @default.
- W4309777854 hasConceptScore W4309777854C126322002 @default.
- W4309777854 hasConceptScore W4309777854C153180895 @default.
- W4309777854 hasConceptScore W4309777854C154945302 @default.
- W4309777854 hasConceptScore W4309777854C2776654903 @default.
- W4309777854 hasConceptScore W4309777854C2779134260 @default.
- W4309777854 hasConceptScore W4309777854C3008058167 @default.
- W4309777854 hasConceptScore W4309777854C38652104 @default.
- W4309777854 hasConceptScore W4309777854C41008148 @default.
- W4309777854 hasConceptScore W4309777854C45942800 @default.
- W4309777854 hasConceptScore W4309777854C524204448 @default.
- W4309777854 hasConceptScore W4309777854C71924100 @default.
- W4309777854 hasLocation W43097778541 @default.
- W4309777854 hasOpenAccess W4309777854 @default.
- W4309777854 hasPrimaryLocation W43097778541 @default.
- W4309777854 hasRelatedWork W2738221750 @default.
- W4309777854 hasRelatedWork W2791691546 @default.
- W4309777854 hasRelatedWork W3034006481 @default.
- W4309777854 hasRelatedWork W3124943098 @default.
- W4309777854 hasRelatedWork W3129712087 @default.
- W4309777854 hasRelatedWork W3162132941 @default.
- W4309777854 hasRelatedWork W4308112567 @default.
- W4309777854 hasRelatedWork W4310989423 @default.
- W4309777854 hasRelatedWork W4321369474 @default.
- W4309777854 hasRelatedWork W4382345315 @default.
- W4309777854 hasVolume "16" @default.
- W4309777854 isParatext "false" @default.
- W4309777854 isRetracted "false" @default.
- W4309777854 workType "article" @default.