Fully Automatic Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images
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http://hdl.handle.net/2183/30142
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Fully Automatic Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray ImagesData
2022Cita bibliográfica
DE MOURA, Joaquim, NOVO, Jorge and ORTEGA, Marcos, 2022. Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images. Applied Soft Computing. 1 January 2022. Vol. 115, p. 108190. ISSN 1568-4946.
https://doi.org/10.1016/j.asoc.2021.108190.
(https://www.sciencedirect.com/science/article/pii/S156849462101036X)
Resumo
[Abstract] Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ± 0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ± 0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology.
Palabras chave
Pulmonary disease detection
Covid-19
Pneumonia
X-ray imaging
Deep learning
Covid-19
Pneumonia
X-ray imaging
Deep learning
Descrición
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
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Dereitos
Atribución-NoComercial-SinDerivadas 4.0 Internacional
ISSN
1568-4946