Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques 1st Edition

Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques (1st Edition) by Mohammad Sufian Badar, Nima Rezaei, Hassan Imtiyaz, Jawed Ahmed, and Afshar Alam is an essential, contemporary guide for anyone bridging medicine and data science. This authoritative volume captures cutting-edge AI and ML approaches tailored to the challenges of COVID-19 detection, monitoring, and analysis.

Discover clear, practical explanations of algorithmic pipelines—from data preprocessing and feature extraction to model selection, validation, and explainability—applied to medical imaging (CT, X-ray), clinical data and epidemiological signals. The book balances rigorous theory with real-world applications, offering case studies, performance evaluation metrics, and discussions on ethics, privacy, and deployment in clinical settings.

Ideal for clinicians, radiologists, data scientists, public-health professionals, and graduate students, this book provides tools to enhance diagnostic workflows and inform pandemic response strategies. Its global perspective makes it relevant to healthcare systems across the United States, Europe, Asia, Africa, and beyond, helping teams implement AI-driven solutions responsibly and effectively.

If you’re seeking a practical, research-informed resource that translates machine learning advances into actionable healthcare insights, this volume delivers. Add Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques to your library to stay at the forefront of AI in healthcare and strengthen your capability to respond to current and future respiratory disease challenges.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.