  Purpose: Since the recent COVID-19 outbreak, there has been an avalanche of
research papers applying deep learning based image processing to chest
radiographs for detection of the disease. To test the performance of the two
top models for CXR COVID-19 diagnosis on external datasets to assess model
generalizability. Methods: In this paper, we present our argument regarding the
efficiency and applicability of existing deep learning models for COVID-19
diagnosis. We provide results from two popular models - COVID-Net and CoroNet
evaluated on three publicly available datasets and an additional institutional
dataset collected from EMORY Hospital between January and May 2020, containing
patients tested for COVID-19 infection using RT-PCR. Results: There is a large
false positive rate (FPR) for COVID-Net on both ChexPert (55.3%) and MIMIC-CXR
(23.4%) dataset. On the EMORY Dataset, COVID-Net has 61.4% sensitivity, 0.54
F1-score and 0.49 precision value. The FPR of the CoroNet model is
significantly lower across all the datasets as compared to COVID-Net -
EMORY(9.1%), ChexPert (1.3%), ChestX-ray14 (0.02%), MIMIC-CXR (0.06%).
Conclusion: The models reported good to excellent performance on their internal
datasets, however we observed from our testing that their performance
dramatically worsened on external data. This is likely from several causes
including overfitting models due to lack of appropriate control patients and
ground truth labels. The fourth institutional dataset was labeled using RT-PCR,
which could be positive without radiographic findings and vice versa.
Therefore, a fusion model of both clinical and radiographic data may have
better performance and generalization.
