In the effort to contain the COVID-19 pandemic, quick and effective diagnosis is paramount in preventing the spread of the disease. While the reverse transcriptase polymerase chain reaction (RT-PCR) test is the gold standard method to identify COVID-19, the use of x-ray radiography (CXR) has been widely used in the clinical workup for patients suspected of infection as an additional means of diagnosis and treatment response monitoring. CXR is available in almost every medical center across the world, allowing a quick and protected means of identifying potential COVID-19 cases to subject to quarantine procedures. However, the major challenge with the use of CXR in COVID-19 diagnosis is its low sensitivity and specificity in current radiological practice due to the similarities in clinical presentation to other diseases. Machine learning methods, particularly deep learning, have been shown to perform extremely well in a variety of classification tasks, often exceeding human performance. To utilize these techniques, a large data set of over 12,000 CXR images, including over 6,000 confirmed COVID-19 positive cases, was collected to train and validate a deep learning model to differentiate COVID-19 pneumonia from other causes of CXR abnormalities. In this work we show that this deep learning method can differentiate between COVID-19 related pneumonia and non-COVID-19 pneumonia, with high sensitivity and specificity.
COVID-19 Pneumonia Diagnosis Using Chest X-ray Radiography and Deep Learning