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 X-rays, and CT-scans. The study further scrutinizes data availability and the precise search strategies employed to create these robust AI models.
Inhaltsverzeichnis (Table of Contents)
- 1. Introduction
- 2. Data Availability
- 3. Search Strategy
- 4. Pre-Process
- 5. Classifier
- 6. Evaluation parameters & criteria
- 7. Future Scope
- 9. Chart Representation
- 10. Conclusion
- References
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This review article aims to assess the use of deep convolutional neural networks (CNNs) for the diagnosis of COVID-19 using chest X-ray and CT-scan images. It explores the feasibility and effectiveness of this approach as a rapid diagnostic tool, considering the limitations of traditional methods like PCR testing during a pandemic.
- The global impact of the COVID-19 pandemic and the need for rapid diagnostic tools.
- Application of deep learning techniques, specifically CNNs, for medical image analysis.
- Comparative analysis of different deep learning approaches for COVID-19 detection.
- Evaluation of the accuracy and efficiency of these deep learning models.
- Future directions and potential improvements in utilizing AI for pandemic response.
Zusammenfassung der Kapitel (Chapter Summaries)
1. Introduction: This chapter introduces the COVID-19 pandemic, highlighting its rapid spread and significant global health impact. It discusses the initial identification of the virus in Wuhan, China, and its subsequent transmission. The chapter emphasizes the limitations of existing diagnostic methods, such as PCR testing, and the urgent need for faster, more accessible diagnostic tools. It also introduces the use of deep learning techniques in medical image analysis as a potential solution, highlighting the benefits of X-ray imaging as a rapid and cost-effective alternative to traditional methods. The severity of the second wave of the pandemic is mentioned, along with the alarmingly high number of cases and deaths globally.
2. Data Availability: [Summary of Chapter 2 would go here if the provided text contained details on Data Availability].
3. Search Strategy: [Summary of Chapter 3 would go here if the provided text contained details on Search Strategy].
4. Pre-Process: [Summary of Chapter 4 would go here if the provided text contained details on Pre-Process].
5. Classifier: [Summary of Chapter 5 would go here if the provided text contained details on Classifier].
6. Evaluation parameters & criteria: [Summary of Chapter 6 would go here if the provided text contained details on Evaluation parameters & criteria].
7. Future Scope: [Summary of Chapter 7 would go here if the provided text contained details on Future Scope].
9. Chart Representation: [Summary of Chapter 9 would go here if the provided text contained details on Chart Representation].
Schlüsselwörter (Keywords)
Deep learning, Convolutional Neural Networks (CNNs), COVID-19, medical image analysis, chest X-ray, CT-scan, image classification, pandemic response, artificial intelligence (AI), diagnostic tools.
Häufig gestellte Fragen
What is the purpose of this document?
This document is a language preview of a review article focusing on the use of deep convolutional neural networks (CNNs) for the diagnosis of COVID-19 using chest X-ray and CT-scan images. It is designed for academic use, outlining the article's structure, objectives, and key themes.
What is included in the Table of Contents?
The Table of Contents includes the following sections: Introduction, Data Availability, Search Strategy, Pre-Process, Classifier, Evaluation parameters & criteria, Future Scope, Chart Representation, Conclusion, and References.
What are the main objectives and key themes of the article?
The article aims to assess the effectiveness of CNNs for COVID-19 diagnosis using chest X-ray and CT-scan images. Key themes include the global impact of the pandemic, application of deep learning, comparative analysis of deep learning approaches, evaluation of model accuracy, and future directions in AI for pandemic response.
What does the "Introduction" chapter cover?
The Introduction chapter covers the rapid spread and global health impact of COVID-19, the limitations of PCR testing, and the potential of deep learning techniques in medical image analysis as a faster and more accessible diagnostic tool.
What is the article about in general?
The article is a language review about using artificial intelligence and deep learning to diagnose COVID-19 infections via imagery.
What are the keywords associated with this review article?
The keywords are: Deep learning, Convolutional Neural Networks (CNNs), COVID-19, medical image analysis, chest X-ray, CT-scan, image classification, pandemic response, artificial intelligence (AI), diagnostic tools.
Are summaries available for all chapters?
No, the provided excerpt only includes a summary of the Introduction chapter. Summaries for other chapters (Data Availability, Search Strategy, Pre-Process, Classifier, Evaluation parameters & criteria, Future Scope, and Chart Representation) are not provided in this language preview.
- Citar trabajo
- Hardik Modi (Autor), Dev Patel (Autor), Sagarkumar Patel (Autor), Vishal Tank (Autor), 2023, Comprehensive and Comparative Review of Covid-19 detection using various deep learning techniques, Múnich, GRIN Verlag, https://www.grin.com/document/1433128
