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The article explores different deep convolutional neural networkarchitectures trained and tested on posteroanterior chest X-rays of 327patients who are healthy (152 patients), diagnosed with COVID-19 (125), andother types of pneumonia (48). In particular, this paper looks at the deepconvolutional neural networks VGG16 and VGG19, InceptionResNetV2 andInceptionV3, as well as Xception, all followed by a flat multi-layer perceptronand a final 30% drop-out. The paper has found that the best performing networkis VGG16 with a final $30$% drop-out trained over 3 classes (COVID-19, NoFinding, Other Pneumonia). It has an internal cross-validated accuracy of$93.9(\pm3.4)$%, a COVID-19 sensitivity of $87.7(-1.9,+2)$%, and a No Findingsensitivity of $96.8(\pm0.8)$%. The respective external cross-validated valuesare $84.1(\pm13.5)$%, $87.7(-1.9,2)$%, and $96.8(\pm0.8)$%. The model optimizerwas Adam with a 1e-4 learning rate, and categorical cross-entropy loss. It ishoped that, once this research will be put to practice in hospitals, healthcareprofessionals will be able in the medium to long-term to diagnosing throughmachine learning tools possible pneumonia, and if detected, whether it islinked to a COVID-19 infection, allowing the detection of new possible COVID-19foyers after the end of possible "stop-and-go" lockdowns as expected by until avaccine is found and widespread. Furthermore, in the short-term, it is hopedpractitioners can compare the diagnosis from the deep convolutional neuralnetworks with possible RT-PCR testing results, and if clashing, a ComputedTomography could be performed as they are more accurate in showing COVID-19pneumonia.

Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rays