article · Scientific Reports
Traditional COVID-19 detection relying on molecular techniques and medical imaging faces specific limitations. A hybrid deep learning method offers automated identification of COVID-19 using whole and partial genome sequences of human coronavirus diseases. The approach converts viral genome sequences into genomic grayscale images through frequency chaos game representation. Deep features are then extracted from these images using the pre-trained AlexNet convolutional neural network, specifically targeting its fifth convolutional and second fully connected layers. Redundant features are eliminated using the ReliefF and least absolute shrinkage and selection operator algorithms before evaluation with decision tree and k-nearest neighbours classifiers. The optimal configuration combines features from the second fully connected layer, selection via the shrinkage operator, and classification using k-nearest neighbours. This setup achieves 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity in distinguishing COVID-19 from other human coronaviruses.
Rapid and accurate detection of coronavirus cases is vital for pandemic control and public health management. By converting genomic data into images and using artificial intelligence for analysis, this method provides an alternative diagnostic approach that bypasses standard limitations of conventional molecular testing and medical scans, delivering near-perfect diagnostic precision across both whole and partial viral genomes.
This computational method could enable automated viral diagnostic software for public health laboratories or genomic surveillance platforms. The technology is at an applied and tested research stage, having demonstrated high accuracy on genomic sequence datasets. Real-world deployment would require integration into existing clinical diagnostic pipelines or laboratory information systems, alongside validation against operational clinical workflows.
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The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.
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DOI: 10.1038/s41598-023-30941-0
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