Coronavirus covid-19 detection by means of explainable deep learning
Abstract The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-27697-y |
| _version_ | 1797952538370113536 |
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| author | Francesco Mercaldo Maria Paola Belfiore Alfonso Reginelli Luca Brunese Antonella Santone |
| author_facet | Francesco Mercaldo Maria Paola Belfiore Alfonso Reginelli Luca Brunese Antonella Santone |
| author_sort | Francesco Mercaldo |
| collection | DOAJ |
| description | Abstract The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95. |
| first_indexed | 2024-04-10T22:47:58Z |
| format | Article |
| id | doaj.art-35f07688964a44e08a2c179685c5ddf9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T22:47:58Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-35f07688964a44e08a2c179685c5ddf92023-01-15T12:11:16ZengNature PortfolioScientific Reports2045-23222023-01-0113111110.1038/s41598-023-27697-yCoronavirus covid-19 detection by means of explainable deep learningFrancesco Mercaldo0Maria Paola Belfiore1Alfonso Reginelli2Luca Brunese3Antonella Santone4Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of MoliseDepartment of Precision Medicine, University of Campania “Luigi Vanvitelli”Department of Precision Medicine, University of Campania “Luigi Vanvitelli”Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of MoliseDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of MoliseAbstract The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.https://doi.org/10.1038/s41598-023-27697-y |
| spellingShingle | Francesco Mercaldo Maria Paola Belfiore Alfonso Reginelli Luca Brunese Antonella Santone Coronavirus covid-19 detection by means of explainable deep learning Scientific Reports |
| title | Coronavirus covid-19 detection by means of explainable deep learning |
| title_full | Coronavirus covid-19 detection by means of explainable deep learning |
| title_fullStr | Coronavirus covid-19 detection by means of explainable deep learning |
| title_full_unstemmed | Coronavirus covid-19 detection by means of explainable deep learning |
| title_short | Coronavirus covid-19 detection by means of explainable deep learning |
| title_sort | coronavirus covid 19 detection by means of explainable deep learning |
| url | https://doi.org/10.1038/s41598-023-27697-y |
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