COVID-XNet: a custom Deep Learning system to diagnose and locate COVID-19 in chest X-ray images
|Author/s||Durán López, Lourdes
Domínguez Morales, Juan Pedro
Corral Jaime, Jesús
Vicente Díaz, Saturnino
Linares Barranco, Alejandro
|Department||Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores|
|Abstract||The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, ...
The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.
|Project ID.||COFNET TEC2016-77785-P
|Citation||Durán López, L., Domínguez Morales, J.P., Corral Jaime, J., Vicente Díaz, S. y Linares Barranco, A. (2020). COVID-XNet: a custom Deep Learning system to diagnose and locate COVID-19 in chest X-ray images. Applied Sciences, 10 (16), 5683-.|