Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images
“Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images” by D. Jude Hemanth, published by Elsevier Science on November 16, 2023, spans 348 pages and is presented in English. This book provides a comprehensive examination of various AI-based methods for analyzing mammogram data, beginning with an overview of the technologies involved. It covers current techniques such as ultrasound, molecular breast imaging, magnetic resonance, and Positron Emission mammography, along with advancements in 3D breast tomosynthesis and 4D mammograms.
Readers will find detailed discussions on deep learning models that enhance the processing of breast images, as well as hybrid intelligence approaches aimed at early-stage cancer detection. The book also explores the application of machine learning classifiers for cancer detection, staging, and density assessment, which are crucial for developing effective treatment plans. Additionally, it addresses the challenges faced by current processing methods that hinder real-time applications, making it a valuable resource for both computer scientists and medical practitioners involved in mammogram analysis.
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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.
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