RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images

El‐Dahshan, El‐Sayed A.; Bassiouni, Mahmoud M.; Hagag, Ahmed; Chakrabortty, Ripon K.; Loh, Hui Wen; Acharya, U. Rajendra

doi:10.1016/j.eswa.2022.117410

Cited by 24 publications

(19 citation statements)

References 57 publications

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“…For example, modern ANNs are much better at recognizing transcription factor binding sites than the earlier approaches mentioned above [ 10 ]. As another recent example, based on chest X-ray images, deep learning methods were successfully applied to classify individuals into one of three groups: coronavirus disease 2019 patients, healthy controls, and individuals with pneumonia [ 55 ].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Overview of frequent pattern mining

Ott

Park

2022

Genomics Inform

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Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.

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Section: Machine Learning Approachesmentioning

confidence: 99%

Overview of frequent pattern mining

Ott

Park

2022

Genomics Inform

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“…The study in [ 72 ] proposed novel architecture using convolutional and temporal neural networks to detect COVID-19 in CXR. For the study, some datasets were combined for a total of 1670 COVID-19 images, 1672 normal images, and 1670 pneumonia images.…”

Section: Covid-19 Prediction Using Deep Learningmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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“…The classification achieved a final accuracy of 97.67%. Musha et al [ 63 ], Canario et al [ 64 ], El-Dahshan et al [ 65 ], and Amin et al [ 66 ] all used CT and CXR images in deep learning models to diagnose COVID-19 infection. These studies obtained accuracies of 97.9%, 98%, 99%, 99.3%, and 96.6%, respectively.…”

Section: Ai For Covid-19 Diagnosismentioning

confidence: 99%

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

2022

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Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60–70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.

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RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images

Cited by 24 publications

References 57 publications

Overview of frequent pattern mining

Overview of frequent pattern mining

A Comprehensive Review of Machine Learning Used to Combat COVID-19

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

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