Abstract
COVID-19 is a rapidly spreading viral disease and has affected over 100
countries worldwide. The numbers of casualties and infected cases have
been escalated particularly in vulnerable states with weakened
healthcare systems. Recently, reverse transcription-polymerase chain
reaction (RT-PCR) is the test of choice for diagnosing COVID-19.
However, current evidence suggests that COVID-19 infected patients are
mostly stimulated from a lung infection after coming in contact with
this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can
be a surrogate in some countries where PCR is not readily available.
This has forced the scientific community to detect COVID-19 infection
from X-ray images and recently proposed machine learning methods offer
great promise for fast and accurate detection. Deep learning with
convolutional neural networks (CNNs) has been successfully applied to
radiological imaging for improving the accuracy of diagnosis. However,
the performance remains limited due to the lack of representative X-ray
images available in public benchmark datasets. To alleviate this issue,
we propose an attention mechanism for data augmentation in the feature
space rather than in the data space using reconstruction independent
component analysis (RICA). Specifically, a unified architecture is
proposed which contains a deep convolutional neural network (CNN), an
attention mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides
the high-level features extracted at the pooling layer where the
attention mechanism chooses the most relevant features and generates
low-dimensional augmented features. Finally, BiLSTM is used to classify
the processed sequential information. We conducted experiments on two
publicly available databases to show that the proposed approach achieves
the state-of-the-art results with an accuracy of 97% and 84% while
being able to generate explanations. Explainability analysis has been
carried out using feature visualization through PCA projection and t-SNE
plots.