Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network.

The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.

Advances in Viral Diagnostic Technologies for Combating COVID-19 and Future Pandemics.

The emergence of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) threatens the health of the global population and challenges our preparedness for pandemic threats. Previous outbreaks of coronaviruses and other viruses have suggested the importance of diagnostic technologies in fighting viral outbreaks. Nucleic acid detection techniques are the gold standard for detecting SARS-CoV-2. Viral antigen tests and serological tests that detect host antibodies have also been developed for studying the epidemiology of COVID-19 and estimating the population that may have immunity to SARS-CoV-2. Nevertheless, the availability, cost, and performance of existing viral diagnostic technologies limit their practicality, and novel approaches are required for improving our readiness for global pandemics. Here, we review the principles and limitations of major viral diagnostic technologies and highlight recent advances of molecular assays for COVID-19. In addition, we discuss emerging technologies, such as clustered regularly interspaced short palindromic repeats (CRISPR) systems, high-throughput sequencing, and single-cell and single-molecule analysis, for improving our ability to understand, trace, and contain viral outbreaks. The prospects of viral diagnostic technologies for combating future pandemic threats are presented.

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans.

The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at https://data.mendeley.com/datasets/3y55vgckg6/1https://data.mendeley.com/datasets/3y55vgckg6/1.