Comprehensive and Comparative Review of Covid-19 detection using various deep learning techniques - Softcover
Inhaltsangabe
Academic Paper from the year 2023 in the subject Computer Science - Miscellaneous, language: English, abstract: Imagine a world where a simple chest X-ray could instantly detect COVID-19, offering a lifeline during a global health crisis. This compelling exploration delves into the groundbreaking application of deep learning and artificial intelligence, specifically convolutional neural networks (CNNs), to revolutionize COVID-19 diagnosis through medical image analysis. As the pandemic strained traditional testing methods like PCR, the urgent need for rapid, accessible diagnostic tools became paramount. This review article meticulously examines the potential of CNNs to analyze chest X-ray and CT-scan images, offering a faster and more cost-effective alternative. Explore a comparative analysis of various deep learning approaches, evaluating their accuracy and efficiency in detecting the virus. Discover how these sophisticated algorithms learn to identify subtle patterns indicative of COVID-19, paving the way for quicker diagnoses and more effective pandemic response strategies. From the initial outbreak in Wuhan to the devastating second wave, witness the evolution of AI-powered diagnostic tools and their crucial role in combating the global health crisis. This article not only highlights the limitations of current methods but also illuminates the future scope of artificial intelligence in pandemic preparedness. Uncover the innovative techniques, evaluation parameters, and future directions shaping the intersection of deep learning and medical imaging. Join the quest to harness the power of technology in the fight against infectious diseases, and witness the potential of AI to transform healthcare. This is more than just image classification; it's a critical step towards a more resilient and responsive global healthcare system, offering hope in the face of unprecedented challenges using cutting-edge tools such as deep learning, convolutional neural networks, chest
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Comprehensive and Comparative Review of Covid-19 detection using various deep learning techniques
Verlag:
GRIN Verlag, GRIN Verlag Dez 2023, 2023
ISBN 10: 3346985989
ISBN 13: 9783346985989
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. Neuware -Academic Paper from the year 2023 in the subject Computer Science - Miscellaneous, , language: English, abstract: The coronavirus disease 2019 (COVID-19), as designated by the World Health Organization, is causing a pandemic that will affect the entire world. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, which is the source of COVID-19, was first identified in late December 2019 in Wuhan, China. Within a few months, the virus had spread to various countries across the world. Because COVID-19 affects millions of individuals worldwide, it has turned into a global health emergency.The most typical symptoms of COVID-19 virus are fever, a dry cough, and gastrointestinal issues. Being extremely contagious, the illness readily spreads to persons in close touch with those who are infected. Contact tracking is a good way to stop the virus from spreading, according to us. Convolutional neural networks (CNNs) in particular have achieved successful outcomes in the categorization and analysis of medical image data using artificial intelligence (AI) approaches.This survey and research proposes a deep CNN architecture for the diagnosis of COVID-19 based on the classification of chest X-ray and CT-Scan images. This review article explains how to use a database of X-ray and CT-Scan images from patients with common bacterial pneumonia to automatically diagnose coronavirus infection., proven Covid-19 infection, and common cases. The study's objective was to assess the value of COVID-19 acquisition. Globally speaking, the number of infected cases increases dramatically in the COVID-19 scenario. Because of this, medical professionals and infected patients made the crucial option to quickly embrace various medical facilities. 28 pp. Englisch. Artikel-Nr. 9783346985989
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Comprehensive and Comparative Review of Covid-19 detection using various deep learning techniques
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Academic Paper from the year 2023 in the subject Computer Science - Miscellaneous, , language: English, abstract: The coronavirus disease 2019 (COVID-19), as designated by the World Health Organization, is causing a pandemic that will affect the entire world. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, which is the source of COVID-19, was first identified in late December 2019 in Wuhan, China. Within a few months, the virus had spread to various countries across the world. Because COVID-19 affects millions of individuals worldwide, it has turned into a global health emergency.The most typical symptoms of COVID-19 virus are fever, a dry cough, and gastrointestinal issues. Being extremely contagious, the illness readily spreads to persons in close touch with those who are infected. Contact tracking is a good way to stop the virus from spreading, according to us. Convolutional neural networks (CNNs) in particular have achieved successful outcomes in the categorization and analysis of medical image data using artificial intelligence (AI) approaches.This survey and research proposes a deep CNN architecture for the diagnosis of COVID-19 based on the classification of chest X-ray and CT-Scan images. This review article explains how to use a database of X-ray and CT-Scan images from patients with common bacterial pneumonia to automatically diagnose coronavirus infection., proven Covid-19 infection, and common cases. The study's objective was to assess the value of COVID-19 acquisition. Globally speaking, the number of infected cases increases dramatically in the COVID-19 scenario. Because of this, medical professionals and infected patients made the crucial option to quickly embrace various medical facilities. Artikel-Nr. 9783346985989
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