Book

The COVID-19 pandemic has accelerated the adoption of Artificial Intelligence (AI) in healthcare, particularly in the analysis of medical imaging and predictive disease modeling. This book provides a comprehensive exploration of state-of-the-art Machine Learning and Deep Learning techniques for COVID-19 diagnosis, severity prediction, and clinical prognosis using chest X-rays, CT scans, and intelligent healthcare analytics. The manuscript discusses AI-based medical imaging approaches, ensemble deep learning architectures, Long Short-Term Memory (LSTM) networks, transfer learning, and optimization techniques for improving diagnostic accuracy and forecasting disease progression. Combining theoretical foundations with practical methodologies, the book presents advanced predictive models for infection forecasting, survival analysis, recovery prediction, and severity classification. It also reviews current research trends, identifies existing research gaps, and highlights future directions in AI-enabled healthcare systems. Designed for researchers, postgraduate students, healthcare professionals, and data scientists, this book serves as a valuable reference for understanding the integration of Artificial Intelligence into modern medical imaging and clinical decision support systems.