Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images

D. Jude Hemanth

• Publisher: Academic Press
• Year: 2023
• Print ISBN: 9780443139994
• eBook ISBN: 9780443140006
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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

Table of contents

• Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images
• Cover
• Title Page
• Copyright
• Contents
• Contributors
• Preface
• Chapter 1 Mammogram data analysis: Trends, challenges, and future directions
• Chapter 2 AI in breast imaging: Applications, challenges, and future research
• Chapter 3 Prediction of breast cancer diagnosis using random forest classifier
• Chapter 4 Medical image analysis of masses in mammography using deep learning model for early diagnosis of cancer tissues
• Chapter 5 A framework for breast cancer diagnostics based on MobileNetV2 and LSTM-based deep learning
• Chapter 6 Autoencoder-based dimensionality reduction in 3D breast images for efficient classification with processing by deep learning architectures
• Chapter 7 Prognosis of breast cancer using machine learning classifiers
• Chapter 8 Breast cancer diagnosis through microcalcification
• Chapter 9 Scrutinization of mammogram images using deep learning
• Chapter 10 Computational techniques for analysis of breast cancer using molecular breast imaging
• Chapter 11 Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging
• Chapter 12 Efficient transfer learning techniques for breast cancer histopathological image classification
• Chapter 13 Classification of breast cancer histopathological images based on shape and texture attributes with ensemble machine learning methods
• Chapter 14 An automatic level set segmentation of breast tumor from mammogram images using optimized fuzzy c-means clustering
• Index