FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms
Abstract The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient’s clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:51 |
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| Enthalten in: |
Applied intelligence - 51(2020), 5 vom: 14. Nov., Seite 2838-2849 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Huang, Zhenxing [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
3D CT image sequences |
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| Anmerkungen: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| doi: |
10.1007/s10489-020-01965-0 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
OLC2125242222 |
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| 520 | |a Abstract The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient’s clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement. | ||
| 650 | 4 | |a Fast assessment network | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a 3D CT image sequences | |
| 650 | 4 | |a Clinical symptoms | |
| 700 | 1 | |a Liu, Xinfeng |4 aut | |
| 700 | 1 | |a Wang, Rongpin |4 aut | |
| 700 | 1 | |a Zhang, Mudan |4 aut | |
| 700 | 1 | |a Zeng, Xianchun |4 aut | |
| 700 | 1 | |a Liu, Jun |4 aut | |
| 700 | 1 | |a Yang, Yongfeng |4 aut | |
| 700 | 1 | |a Liu, Xin |4 aut | |
| 700 | 1 | |a Zheng, Hairong |4 aut | |
| 700 | 1 | |a Liang, Dong |4 aut | |
| 700 | 1 | |a Hu, Zhanli |4 aut | |
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