Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images

This book explores AI-based methods for analyzing mammogram images, focusing on advancements in technology and deep learning applications for disease detection.

The book Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images provides a thorough exploration of various AI-driven methods for analyzing mammogram data in medical contexts. It begins with a foundational overview of mammogram data analysis, paving the way for an in-depth discussion of current technologies such as ultrasound, molecular breast imaging (MBI), magnetic resonance (MR), and Positron Emission mammography (PEM). Additionally, it highlights recent advancements in 3D breast tomosynthesis and 4D mammograms, showcasing the evolution of imaging techniques in the fight against breast cancer.

Each chapter of Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images introduces deep learning models that demonstrate their potential in enhancing the efficiency of breast image processing. The book also delves into hybrid intelligence approaches that facilitate early-stage detection of breast cancer. By integrating machine learning classifiers, the text addresses critical aspects of cancer detection, staging, and density assessment, all of which are essential for formulating effective treatment plans.

This comprehensive resource is designed not only for computer scientists and medical practitioners aiming to develop real-time AI-based mammogram analysis systems but also for those interested in understanding the challenges and limitations of existing processing methods. Through its detailed examination, the book aspires to bridge the gap between technology and practical medical applications, ultimately contributing to improved patient outcomes in breast cancer detection and treatment.