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A Comprehensive Survey on COVID-19 Detection and Classification Using Chest-X-Ray Images
Traitement Du Signal ; 39(4):1407-1419, 2022.
Article in English | Web of Science | ID: covidwho-2091157
ABSTRACT
The World Health Organization (WHO) made the announcement that the SARS virusinduced Coronavirus contamination-2019 (COVID-19) has been raised to the status of an international pandemic in March of 2020. If this virus is discovered at a relatively early stage in its life cycle, then it will be possible to contain it and treat it in an effective manner. Because of this fact, real-time polymerase chain reaction (RT-PCR) has emerged as the screening method of first desire for the rapid detection of COVID-19 in blood samples. This is a direct result of the fact Large-scale research have shown that the outcomes of an RTPCR experiment can be misleadingly bad up to sixty two percent of the time. As a final result, the focus of these studies is on a thorough examination of COVID-19 detection and complexity through the utilisation of photos obtained from chest x-rays that are centred at the lung. In the beginning, the research looked into the many layers of computer-aided detection, such as deep learning and meta-heuristics techniques. After that, the research is concentrated on the processes of feature extraction, feature preference, and sophistication operations by utilising techniques such as gadget learning, deep learning, and biooptimization. The study highlights the contemporary difficulties presented by artificial intelligence structures for the detection of COVID-19, which can help to put into action a hybrid system.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Observational study Language: English Journal: Traitement Du Signal Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Observational study Language: English Journal: Traitement Du Signal Year: 2022 Document Type: Article