Deep Learning Frameworks for COVID-19 Chest X-Ray Analytics

Deep Learning Frameworks for COVID-19 Chest X-Ray Analytics explores the application of deep learning and artificial intelligence techniques to the analysis of chest X-ray images associated with COVID-19. The book introduces fundamental concepts in medical image analysis, deep neural networks, image classification, feature extraction, and computer vision, with emphasis on computational approaches for examining chest radiographic data. It discusses the preparation and analysis of X-ray images, development of deep learning models, classification strategies, and evaluation of model performance. Attention is given to how learned visual features can support the computational identification and analysis of patterns in chest X-ray datasets. The book also considers important aspects of medical imaging workflows, including image preprocessing, dataset organization, model validation, and interpretation of analytical results. By connecting deep learning frameworks with chest X-ray analytics, this book provides a technical reference for students, researchers, data scientists, biomedical engineers, and professionals working in medical imaging, healthcare artificial intelligence, computer vision, and clinical data analysis. The material is presented as a foundation for understanding AI-driven approaches to radiographic image analytics.