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 X-rays, and CT-scans. The study further scrutinizes data availability and the precise search strategies employed to create these robust AI models.

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 X-rays, and CT-scans. The study further scrutinizes data availability and the precise search strategies employed to create these robust AI models.