Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images
This work addresses early diagnosis of COVID-19 for healthcare applications, but it appears incremental as it applies a standard CNN approach to a specific medical imaging task.
The authors tackled the problem of detecting COVID-19 from chest X-ray images by proposing a randomly initialized convolutional neural network, achieving accuracies of 94% on the COVIDx dataset and 99% on the enhanced COVID-19 dataset.
By the start of 2020, the novel coronavirus disease (COVID-19) has been declared a worldwide pandemic. Because of the severity of this infectious disease, several kinds of research have focused on combatting its ongoing spread. One potential solution to detect COVID-19 is by analyzing the chest X-ray images using Deep Learning (DL) models. In this context, Convolutional Neural Networks (CNNs) are presented as efficient techniques for early diagnosis. In this study, we propose a novel randomly initialized CNN architecture for the recognition of COVID-19. This network consists of a set of different-sized hidden layers created from scratch. The performance of this network is evaluated through two public datasets, which are the COVIDx and the enhanced COVID-19 datasets. Both of these datasets consist of 3 different classes of images: COVID19, pneumonia, and normal chest X-ray images. The proposed CNN model yields encouraging results with 94% and 99% of accuracy for COVIDx and enhanced COVID-19 dataset, respectively.