Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences

Gomes, Juliana Carneiro; Masood, Aras Ismael; Silva, Leandro Honorato de S.; Ferreira, Janderson; Júnior, Agostinho A. F.; Rocha, Allana Lais dos Santos; Oliveira, Lívia Costa de; Silva, Nathália Regina Cauás da; Fernandes, Bruno; Santos, Wellington Pinheiro dos

doi:10.1038/s41598-021-90766-7

Cited by 26 publications

(14 citation statements)

References 78 publications

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“…These last are the extracted features utilized as input to five types of shallow supervised classifiers, namely, SVM, RF, MLP, Naïve Bayes classifier and Instance-Based-K (IBK) learner ( Alpaydin, 2014 ). Interestingly, the multiclass classification tests carried out in Gomes et al (2021) support the conclusion that RF classifiers are the most performing ones and they attain average accuracies around 94%.…”

Section: Related Workmentioning

confidence: 62%

“…Finally, the follow-up paper in Gomes et al (2021) extends the utilization of shallow ML approaches to the classification of DNA sequences of 25 different virus classes. Specifically, the paper proposes a technique for representing DNA sequences in which each sequence is partitioned into shorter mini-sequences that partially overlap in a pseudo-convolutional fashion, in order to be represented by suitable co-occurrence matrices.…”

Section: Related Workmentioning

confidence: 99%

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A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Scarpiniti

Ahrabi

Baccarelli

et al. 2022

Expert Systems with Applications

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Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.

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Section: Related Workmentioning

confidence: 62%

Section: Related Workmentioning

confidence: 99%

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Scarpiniti

Ahrabi

Baccarelli

et al. 2022

Expert Systems with Applications

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show abstract

“…Besides the medical imaging modalities and molecular techniques, various studies have detected COVID-19 by extracting features from its genome sequence [26][27][28][29][30][31] . Arslan et al 26 presented a system to detect COVID-19, among other HCoV diseases, by extracting CpG-based features from whole genome sequences.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…Not only that, Gomes et al [45] proposed an AI framework for texture analysis of the CoVID-19 chest X-ray images, however, for diagnosis, it is not the best approach. Therefore, they gradually shifted to the pseudo-convolutional machines with the help of RT-PCR results to characterize the virus sequences [46]. In the meantime, Ismael et al [47] published a survey paper to show the performance of the multiresolution approaches on CoVID-19 chest X-ray images which shows sometimes image resolution creates a major issue in detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

CoWarriorNet: A Novel Deep-Learning Framework for CoVID-19 Detection from Chest X-Ray Images

Roy¹,

Shai

Ghosh³

et al. 2021

New Gener. Comput.

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Even after scavenging the existence of mankind for the past year, the wrath of CoVID-19 is yet to die down. Countries like India are still getting haunted by the devastating conundrum, with coronavirus ripping through its citizens in the concurrent second wave. The surge of cases has prompted rapid intervention, with medical authorities pushing it to the limit to curve a roadblock to its aggressive growth. But, even after effortless work, human intervention remains slow and insufficient. Furthermore, relevant testing methodologies have shown weakness while detecting threats, with the recent growth of post-Covid complexities, thereby leaving a painful mark. This as such created a major requirement for technological advancements, which can cater to the mass. The growth of computational prowess in the past decade made the field of Deep Learning a major contributor in curving out algorithms to solve this. Adding to the excellent foundation of Deep Learning, this paper, proposes a novel CoWarriorNet model for rapid detection of CoVID-19, via chest X-ray images, which adds in an extra layer of precision and confirmation in the detection of cases in both pre-Covid and post-Covid conditions. The proposed classification model curves out an excellent accuracy of 97.8%, with the major eye-candy being the sensitivity rate of 0.99 when detecting CoVID-19 cases. This model introduces a new concept of Alpha Trimmed Average Pooling, which along with the novel architecture adds a subtle touch to its high efficiency, thereby giving a much-needed solution to the medical experts. The two-mouthed architecture provides the added benefit of a confidence score, deducing human aid in case of discrepancy.

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Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences

Cited by 26 publications

References 78 publications

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

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

CoWarriorNet: A Novel Deep-Learning Framework for CoVID-19 Detection from Chest X-Ray Images

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