COVID-19 virus has rapidly spread in mainland China and into multiple countries worldwide. As
of April 7th 2020 in Italy, one of the most severely affected countries, 135,586 Patients
with COVID19 were recorded, and 17,127 of them died; at the time of writing Piedmont is the
3rd most affected region in Italy, with 13,343 recorded cases. Early diagnosis is a key
element for proper treatment of the patients and prevention of the spread of the disease.
Given the high tropism of COVID-19 for respiratory airways and lung epithelium,
identification of lung involvement in infected patients can be relevant for treatment and
monitoring of the disease. Virus testing is currently considered the only specific method of
diagnosis. The Center for Disease Control (CDC) in the US recommends collecting and testing
specimens from the upper respiratory tract (nasopharyngeal and oropharyngeal swabs) or from
the lower respiratory tract when available (bronchoalveolar lavage, BAL) for viral testing
with reverse transcription polymerase chain reaction (RT-PCR) assay. Current position papers
from radiological societies (Fleischner Society, SIRM, RSNA) do not recommend routine use of
imaging for COVID-19 diagnosis.

However, it has been widely demonstrated that, even at early stages of the disease, chest
x-rays (CXR) and computed tomography (CT) scans can show pathological findings. It should be
noted that they are actually non specific, and overlap with other viral infections (such as
influenza, H1N1, SARS and MERS): most authors report peripheral bilateral ill-defined and
ground-glass opacities, mainly involving the lower lobes, progressively increasing in
extension as disease becomes more severe and leading to diffuse parenchymal consolidation, CT
is a sensitive tool for early detection of peripheral ground glass opacities; however routine
role of CT imaging in these Patients is logistically challenging in terms of safety for
health professionals and other patients, and can overwhelm available resources. Chest X-ray
can be a useful tool, especially in emergency settings: it can help exclude other possible
lung "noxa", allow a first rough valuation of the extent of lung involvement and most
importantly can be obtained at patients bed using portable devices, limiting possible
exposure in health care workers and other patients. Furthermore, CXR can be repeated over
time to monitor the evolution of lung disease.

Methodology:

we describe the deeplearning approach based on quite standard pipeline, namely chest image
pre-processing and lung segmentation followed by classification model obtained with transfer
learning. As we will see in this section, data pre-processing is fundamental to remove any
bias present in the data. In particular, we will show that it is easy for a deep model to
recognize these biases which drive the learning process. Given the small size of COVID
datasets, a key role is played by the larger datasets used for pre-training. Therefore, we
first discuss which datasets can be used for our goals.