ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.
Comput Biol Med
; 153: 106338, 2023 Feb.
Artigo
em Inglês
| MEDLINE | ID: covidwho-2122404
ABSTRACT
Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.
Palavras-chave
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
MEDLINE
Assunto principal:
COVID-19
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
Limite:
Humanos
Idioma:
Inglês
Revista:
Comput Biol Med
Ano de publicação:
2023
Tipo de documento:
Artigo
País de afiliação:
J.compbiomed.2022.106338
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