Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods

Khodaei, Amin; Shams, Parvaneh; Sharifi, Hadi; Tazehkand, Behzad Mozaffari

doi:10.1016/j.bspc.2022.104192

Cited by 9 publications

(10 citation statements)

References 44 publications

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“…Comparison with the earlier COVID-19 studies. In this section, the proposed system is compared with the earlier studies that used genome sequences to detect COVID-19 [26][27][28][29][30][31][32][33][34][35]38,39 . The results of all reviewed studies are summarized in Table 11.…”

Section: Resultsmentioning

confidence: 99%

“…Khodaei et al 34 presented an effective system to classify COVID-19 among influenza cases. The length of the whole genome sequences of COVID-19 and influenza viruses was about 13,000 and 30,000 base pairs.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, genomic signal processing (GSP) techniques have been used to detect COVID-19 [32][33][34][35] . These techniques transform the genome sequences into genomic signals using various genomic signal mapping approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Khodaei et al 34 used the Z-curve technique to transform the whole genome sequences into genomic signals to classify COVID-19 and influenza cases. The Z-curve transforms the genome sequence from the nucleotides into three signal vectors (X, Y, and Z) based on the vector of each nucleotide in the sequence (A, T, C, and G).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The previous COVID-19 studies [26][27][28][29][30][31][32][33][34]38 had limitations related to the analysis of partial genome sequences of HCoV diseases. For instance, if a partial COVID-19 sequence is fed to the system, the system cannot predict whether the patient has COVID-19 or not.…”

Section: Main Contributionmentioning

confidence: 99%

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A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques

Hammad

Ghoneim

Mabrouk

et al. 2023

Sci Rep

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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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