Improving Predictive Detection of Leukemia Using Critical Thinking Models

Improving Predictive Detection of Leukemia Using Critical Thinking Models is a comprehensive anthology that delves into the forefront of healthcare research, focusing on the critical task of accurately detecting leukemia. With leukemia ranking among the deadliest diseases globally, this book presents an essential collection of studies aimed at saving lives and ensuring proper treatment through advanced detection methods. Drawing upon the expertise of medical professionals, researchers, and scholars, this anthology explores the utilization of cutting-edge AI/ML/DL algorithms and techniques in medical image segmentation, feature selection, and classification specific to leukemia. From algorithms for leukemia detection to image segmentation and follow-up treatment using various examinations, the book covers crucial topics that resonate with medical practitioners, policymakers, academicians, and students alike. By providing knowledge with a groundwork of the fundamentals, and progressing to advanced levels, Improving Predictive Detection of Leukemia Using Critical Thinking Models fosters readers' critical thinking abilities and empowers them to develop innovative detection models for leukemia. This book serves as a valuable resource for both new researchers, who will gain essential insights into the field, and experienced professionals seeking to analyze algorithm performance and identify future research directions. Catering to a diverse audience, including medical professionals, nurses, oncologists, policymakers, researchers, academicians, scholars, practitioners, instructors, and students at all levels of study, this anthology facilitates interdisciplinary collaboration and contributes to the research society's collective understanding of leukemia detection.