R. Rajmohan - Böcker

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

This book provides a comprehensive discussion of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, DL methods for medical image analysis, and DL-based clinical computer-aided diagnosis systems. It further presents algorithms, models, software, and tools in the field of bioinformatics.This book:Presents mathematical principles of DL algorithms such as convolutional neural networks and recurrent neural networksDiscusses applications of DL such as hyperparameter optimization and multimodal DL for bioinformaticsShowcases how algorithms are applied to a broad range of application areas, including microscopy and pathologyCovers DL techniques such as deep feedforward networks, sequence modeling, and convolutional networksExamines the importance of deep learning in biomedical image processing and enhancing biological diagnosisIt will serve as an ideal reference text for senior undergraduates, graduate students, and academic researchers in the areas such as electrical engineering, electronics, and communications engineering, computer engineering, and information technology.