Since December 2019, the outbreak of a new coronavirus, named novel coronavirus 2019 (COVID-19), has rapidly spread across China and other countries across the globe [1–4]. As of 18 May, 4,618,821 cases of COVID-19 with 311,847 deaths are have been reported [5]. Since the World Health Organization declared the COVID-19 outbreak as a public health emergency of international concern, namely a pandemic, countries around the globe have heightened their surveillance to quickly diagnose potential new cases of COVID-19. Due to increasing outbreak of COVID-19, the early diagnosis of patients is crucial for prompt and effective prevention and control of COVID-19. Presently, nucleic acid testing is generally considered as diagnostic ground truth. However, the stringent requirements of transportation and storage of COVID-19 nucleic acid kits may constitute an unsurmountable challenge for many existing transportation and hospital facilities in crisis. Moreover, the methodology, disease development stages and the method of sample collection could impact the result of nucleic acid testing [6]. The reverse transcription polymerase chain reaction (RT-PCR) could be used for identification of COVID-19, but it is difficult to identify the severity of COVID-19 patients, to predict whether the patient should be transferred to ICU or would need ventilators soon. These factors prolong the time to control the spread of COVID-19 and increase the recovery time of patients.
Chest CT, especially, high-resolution CT, is an important tool to detect the lung changes of 2019 novel coronavirus pneumonia (NCP) and to aid in evaluating the nature and extension of lesions. In a recent report, Ai et al. [7] utilized CT scans to investigate its diagnostic value and consistency in comparison with RT-PCR assay for COVID-19. It has been found that of 1,014 patients, 59% had positive RT-PCR results while 88% had positive chest CT scans which means chest CT has a high sensitivity for diagnosis of COVID-19. Hence, the Chest CT may be treated as a primary tool to detect COVID-19 in epidemic areas. Some other investigators focused on the understanding of virus infection pathogenesis by observing the imaging patterns on chest CT. Bernheim et al. [8] characterized chest CT findings in 121 COVID-19 infected patients in relationship to the time between symptom onset and the initial CT scan. Pan et al. [9] investigated the lung abnormalities by observing the changes in chest CT of patients from initial diagnosis to recovery. It was observed that the lung abnormalities on chest CT showed greatest severity approximately 10 days after initial onset of symptoms. Most of the concern in recent reports are with the diagnosis of the COVID-19 or the clinical observation during the therapeutic treatment [10–13].
Although for most COVID-19 patients, the clinical symptoms are mild and the prognosis is good, about 20% can develop into severe cases with the symptoms of pneumonia, pulmonary edema, septic shock, metabolic acidosis, acute respiratory distress syndrome or even death [14]. Therefore, the timely diagnosis, accurate assessment with the following symptomatic treatment is very important and is the key to improve the prognosis and reduce the mortality.
It is known that convolutional neural networks (CNNs) are quite powerful in data mining, image classification/detection, and computer vision. Many research groups have applied deep learning methods into COVID-19 computer aided diagnosis [13, 15–17]. But to our best knowledge, no studies were focused on the identification of severity of infected patients, although this identification is a crucial evaluation criterion to develop proper therapeutic treatment strategy. Therefore, developing a rapid, accurate and automatic tool for severity screening is both an urgent and essential task, which could help physicians anticipating the need for ICU admission. Thus, to achieve an accurate and efficient COVID-19 severity diagnosis, we classified features gained from pre-trained CNNs such as Inception v3 [18], ResNet [19] and DenseNet [20] to identify the severity of COVID-19 patients in this study. Amid the crisis in hospitals and due to challenges of training a network from scratch (e.g., necessity of a large dataset), we find this approach to be more practical and reliable.