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COVID-19 Diagnosis Using Transfer-Learning Techniques
Intelligent Automation and Soft Computing ; 29(3):649-667, 2021.
Artigo em Inglês | Web of Science | ID: covidwho-1326165
ABSTRACT
COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two-and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis systems achieved excellent results with the VGG-19 architecture. The two-classifier diagnosis system achieved high sensitivity for COVID-19, with 99.5% and 100% on DATASET-1 and DATASET-2, respectively. The three-classifier diagnosis system achieves high sensitivity for COVID-19, with 98.4% and 100% on DATASET-1 and DATASET-2, respectively. The high sensitivity of these diagnostic systems for COVID-19 will significantly improve the speed and precision of COVID-19 diagnosis.

Texto completo: Disponível Coleções: Bases de dados de organismos internacionais Base de dados: Web of Science Tipo de estudo: Estudo diagnóstico Idioma: Inglês Revista: Intelligent Automation and Soft Computing Ano de publicação: 2021 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados de organismos internacionais Base de dados: Web of Science Tipo de estudo: Estudo diagnóstico Idioma: Inglês Revista: Intelligent Automation and Soft Computing Ano de publicação: 2021 Tipo de documento: Artigo