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Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images
Article

COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images

1
Robotics and Tech. of Computers Lab, ETSII-EPS, Universidad de Sevilla, 41011 Seville, Spain
2
Servicio de Oncología Médica, Clinica Universidad de Navarra, 28027 Madrid, Spain
3
Smart Computer Systems Researh and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), Universidad de Sevilla, 41012 Seville, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5683; https://doi.org/10.3390/app10165683
Received: 15 July 2020 / Revised: 7 August 2020 / Accepted: 13 August 2020 / Published: 16 August 2020
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. View Full-Text
Keywords: COVID-19; deep learning; convolutional neural networks; medical image analysis; computer-aided diagnosis; X-ray COVID-19; deep learning; convolutional neural networks; medical image analysis; computer-aided diagnosis; X-ray
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MDPI and ACS Style

Duran-Lopez, L.; Dominguez-Morales, J.P.; Corral-Jaime, J.; Vicente-Diaz, S.; Linares-Barranco, A. COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images. Appl. Sci. 2020, 10, 5683. https://doi.org/10.3390/app10165683

AMA Style

Duran-Lopez L, Dominguez-Morales JP, Corral-Jaime J, Vicente-Diaz S, Linares-Barranco A. COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images. Applied Sciences. 2020; 10(16):5683. https://doi.org/10.3390/app10165683

Chicago/Turabian Style

Duran-Lopez, Lourdes, Juan P. Dominguez-Morales, Jesús Corral-Jaime, Saturnino Vicente-Diaz, and Alejandro Linares-Barranco. 2020. "COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images" Applied Sciences 10, no. 16: 5683. https://doi.org/10.3390/app10165683

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