COVID Detection From Chest X-Ray Images Using Multi-Scale Attention

Deep learning based methods have shown great promise in achieving accurate automatic detection of Coronavirus Disease (
covid
) - 19 from Chest X-Ray (
cxr
) images.However, incorporating explainability in these solutions remains relatively less explored. We present a hierarchical classification approach for separating normal, non-
covid
pneumonia (
ncp
) and
covid
cases using
cxr
images. We demonstrate that the proposed method achieves clinically consistent explainations. We achieve this using a novel multi-scale attention architecture called Multi-scale Attention Residual Learning (
marl
) and a new loss function based on conicity for training the proposed architecture. The proposed classification strategy has two stages. The first stage uses a model derived from DenseNet to separate pneumonia cases from normal cases while the second stage uses the
marl
architecture to discriminate between
covid
and
ncp
cases. With a five-fold cross validation the proposed method achieves 93%, 96.28%, and 84.51% accuracy respectively over three large, public datasets for normal vs.
ncp
vs.
covid
classification. This is competitive to the state-of-the-art methods. We also provide explanations in the form of GradCAM attributions, which are well aligned with expert annotations. The attributions are also seen to clearly indicate that
marl
deems the peripheral regions of the lungs to be more important in the case of
covid
cases while central regions are seen as more important in
ncp
cases. This observation matches the criteria described by radiologists in clinical literature, thereby attesting to the utility of the derived explanations.