Computational intelligence and modelling techniques for disease detection in mammogram images

• Titel: Computational intelligence and modelling techniques for disease detection in mammogram images
• Mitwirkende: Hemanth, D. Jude [MitwirkendeR]
• Verf.angabe: edited by D. Jude Hemanth
• Verlagsort: London
• Verlag: Academic Press
• Jahr: 2024
• Umfang: 1 online resource
• Fussnoten: Includes index. - Print version record
• ISBN: 0-443-14000-6
• ISBN: 0-443-13999-7
• ISBN: 978-0-443-13999-4
• ISBN: 978-0-443-14000-6
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Sein ; Cancer ; Imagerie
• Sach-SW: Intelligence artificielle ; Applications en médecine
• K10plus-PPN: 1899110143
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4568081408
• Lokale URL Inst.: Zum Volltext

Abstract

Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images comprehensively examines the wide range of AI-based mammogram analysis methods for medical applications. Beginning with an introductory overview of mammogram data analysis, the book covers the current technologies such as ultrasound, molecular breast imaging (MBI), magnetic resonance (MR), and Positron Emission mammography (PEM), as well as the recent advancements in 3D breast tomosynthesis and 4D mammogram. Deep learning models are presented in each chapter to show how they can assist in the efficient processing of breast images. The book also discusses hybrid intelligence approaches for early-stage detection and the use of machine learning classifiers for cancer detection, staging and density assessment in order to develop a proper treatment plan. This book will not only aid computer scientists and medical practitioners in developing a real-time AI based mammogram analysis system, but also addresses the issues and challenges with the current processing methods which are not conducive for real-time applications. Presents novel ideas for AI based mammogram data analysis Discusses the roles deep learning and machine learning techniques play in efficient processing of mammogram images and in the accurate defining of different types of breast cancer Features dozens of real-world case studies from contributors across the globe.