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Randomly initialized convolutional neural network for the recognition of COVID ‐19 using X‐ray images
Author(s) -
Ben Atitallah Safa,
Driss Maha,
Boulila Wadii,
Ben Ghézala Henda
Publication year - 2022
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22654
Subject(s) - convolutional neural network , covid-19 , computer science , artificial intelligence , pattern recognition (psychology) , artificial neural network , medicine , disease , pathology , infectious disease (medical specialty)
By the start of 2020, the novel coronavirus (COVID‐19) had been declared a worldwide pandemic, and because of its infectiousness and severity, several strands of research have focused on combatting its ongoing spread. One potential solution to detecting COVID‐19 rapidly and effectively is by analyzing chest X‐ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) have been presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this study, we propose a novel randomly initialized CNN (RND‐CNN) architecture for the recognition of COVID‐19. This network consists of a set of differently‐sized hidden layers all created from scratch. The performance of this RND‐CNN is evaluated using two public datasets: the COVIDx and the enhanced COVID‐19 datasets. Each of these datasets consists of medical images (X‐rays) in one of three different classes: chests with COVID‐19, with pneumonia, or in a normal state. The proposed RND‐CNN model yields encouraging results for its accuracy in detecting COVID‐19 results, achieving 94% accuracy for the COVIDx dataset and 99% accuracy on the enhanced COVID‐19 dataset.