Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images

• URL: http://arxiv.org/abs/2105.08199v1
• Date: Mon, 17 May 2021 23:40:37 GMT
• Title: Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images
• Authors: Safa Ben Atitallah, Maha Driss, Wadii Boulila, Henda Ben Gh\'ezala
• Abstract summary: coronavirus disease (COVID-19) has been declared a worldwide pandemic. One potential solution to detect COVID-19 is by analyzing the chest X-ray images using Deep Learning (DL) models. In this study, we propose a novel randomly CNN architecture for the recognition of COVID-19. The proposed CNN model yields encouraging results with 94% and 99% of accuracy for COVIDx and enhanced COVID-19 dataset, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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