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.
1. Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Preface - p. xvii 8. Chapter 1 Mammogram data analysis: Trends, challenges, and future directions - p. 1 9. Chapter 2 AI in breast imaging: Applications, challenges, and future research - p. 39 10. Chapter 3 Prediction of breast cancer diagnosis using random forest classifier - p. 55 11. Chapter 4 Medical image analysis of masses in mammography using deep learning model for early diagnosis of cancer tissues - p. 75 12. Chapter 5 A framework for breast cancer diagnostics based on MobileNetV2 and LSTM-based deep learning - p. 91 13. Chapter 6 Autoencoder-based dimensionality reduction in 3D breast images for efficient classification with processing by deep learning architectures - p. 111 14. Chapter 7 Prognosis of breast cancer using machine learning classifiers - p. 129 15. Chapter 8 Breast cancer diagnosis through microcalcification - p. 151 16. Chapter 9 Scrutinization of mammogram images using deep learning - p. 177 17. Chapter 10 Computational techniques for analysis of breast cancer using molecular breast imaging - p. 203 18. Chapter 11 Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging - p. 235 19. Chapter 12 Efficient transfer learning techniques for breast cancer histopathological image classification - p. 259 20. Chapter 13 Classification of breast cancer histopathological images based on shape and texture attributes with ensemble machine learning methods - p. 277 21. Chapter 14 An automatic level set segmentation of breast tumor from mammogram images using optimized fuzzy c-means clustering - p. 299 22. Index - p. 323
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.
1. Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Preface - p. xvii 8. Chapter 1 Mammogram data analysis: Trends, challenges, and future directions - p. 1 9. Chapter 2 AI in breast imaging: Applications, challenges, and future research - p. 39 10. Chapter 3 Prediction of breast cancer diagnosis using random forest classifier - p. 55 11. Chapter 4 Medical image analysis of masses in mammography using deep learning model for early diagnosis of cancer tissues - p. 75 12. Chapter 5 A framework for breast cancer diagnostics based on MobileNetV2 and LSTM-based deep learning - p. 91 13. Chapter 6 Autoencoder-based dimensionality reduction in 3D breast images for efficient classification with processing by deep learning architectures - p. 111 14. Chapter 7 Prognosis of breast cancer using machine learning classifiers - p. 129 15. Chapter 8 Breast cancer diagnosis through microcalcification - p. 151 16. Chapter 9 Scrutinization of mammogram images using deep learning - p. 177 17. Chapter 10 Computational techniques for analysis of breast cancer using molecular breast imaging - p. 203 18. Chapter 11 Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging - p. 235 19. Chapter 12 Efficient transfer learning techniques for breast cancer histopathological image classification - p. 259 20. Chapter 13 Classification of breast cancer histopathological images based on shape and texture attributes with ensemble machine learning methods - p. 277 21. Chapter 14 An automatic level set segmentation of breast tumor from mammogram images using optimized fuzzy c-means clustering - p. 299 22. Index - p. 323
