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?:abstract
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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
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