Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning.
J Healthc Eng
; 2021: 5528441, 2021.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1211612
ABSTRACT
Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Interpretación de Imagen Radiográfica Asistida por Computador
/
Tomografía Computarizada por Rayos X
/
Aprendizaje Profundo
/
COVID-19
Tipo de estudio:
Estudios diagnósticos
/
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
J Healthc Eng
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
2021
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