| Téléchargement | - Voir la version finale : COVID-19 detection from chest x-ray images using deep convolutional neural networks with weights imprinting approach (PDF, 1.0 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.15353/jcvis.v6i1.3546 |
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| Auteur | Rechercher : As'ad, Hala; Rechercher : Azmi, Hilda; Rechercher : Xi, Pengcheng1; Rechercher : Ebadi, Ashkan1; Rechercher : Tremblay, Stéphane1; Rechercher : Wong, Alexander |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | CVIS 2020 - Special Session on Computer Vision and Intelligent Systems at the 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020), March 23-26, 2020, Phuket, Thailand |
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| Description physique | 3 p. |
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| Résumé | COVID-19 pandemic has drastically changed our lives. Chest radiography has been used to detect COVID-19. However, the number of publicly available COVID-19 x-ray images is extremely limited, resulting in a highly imbalanced dataset. This is a challenge when using deep learning for classification and detection. In this work, we propose the use of pre-trained deep Convolutional Neural Networks (CNN) and integrate them with a few-shot learning approach named imprinted weights. The integrated model is fine tuned to enhance the capability of detecting COVID-19. The proposed solution then combines the fine-tuned models using a weighted average ensemble for achieving an optimal 82% sensitivity to COVID-19. To the best of authors’ knowledge, the proposed solution is one of the first to utilize imprinted weights model with weighted average ensemble for enhancing the model sensitivity to COVID-19. |
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| Date de publication | 2021-01-15 |
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| Maison d’édition | University of Waterloo |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 85296412-4e4e-419b-9367-8b68ae8838f0 |
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| Enregistrement créé | 2021-11-15 |
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| Enregistrement modifié | 2021-11-15 |
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