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In this study, the problem of automatically classifying pulmonary diseases,including the recently emerged COVID-19, from X-Ray images, is considered.While the spread of COVID-19 is increased, new, automatic, and reliable methodsfor accurate detection are essential to reduce the exposure of the medicalexperts to the outbreak. X-ray imaging, although limited to specificvisualizations, may be helpful for the diagnosis. Deep Learning has proven tobe a remarkable method to extract massive high-dimensional features frommedical images. Specifically, in this paper, the state-of-the-art ConvolutionalNeural Network called Mobile Net is employed and trained from scratch toinvestigate the importance of the extracted features for the classificationtask. A large-scale dataset of 3905 X-Ray images, corresponding to 6 diseasesis utilized for training MobileNet v2, which has been proven to achieveremarkable results in related tasks. The results suggest that training CNNsfrom scratch may reveal vital biomarkers related but not limited to theCOVID-19 disease, while an overall classification accuracy of the seven classesreaches 87.66%. Besides, this method achieves 99.18% accuracy, 97.36%Sensitivity, and 99.42% Specificity in the detection of COVID-19.

Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases