Deep learning-based COVID-19 diagnosis using CT scans with laboratory and physiological parameters
Iet Image Processing
; 2023.
Artículo
en Inglés
| Web of Science | ID: covidwho-20242362
ABSTRACT
The global economy has been dramatically impacted by COVID-19, which has spread to be a pandemic. COVID-19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription-polymerase chain reaction (RT-PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID-19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID-19 having an accuracy, sensitivity, specificity, and F1-score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches.
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Web of Science
Tipo de estudio:
Estudios diagnósticos
/
Estudio pronóstico
Idioma:
Inglés
Revista:
Iet Image Processing
Año:
2023
Tipo del documento:
Artículo
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