Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images

1st Edition - November 16, 2023
Imprint: Academic Press
Editor: D. Jude Hemanth
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 3 9 9 9 - 4
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 4 0 0 0 - 6

Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images comprehensively examines the wide range of AI-based mammogram analysis methods f… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter 1. Mammogram data analysis: Trends, challenges, and future directions
1. Introduction
2. Related works
3. Current trends in mammography analysis
4. Challenges in mammogram data analysis
5. Future directions of mammogram analysis
6. Conclusion
Chapter 2. AI in breast imaging: Applications, challenges, and future research
1. Introduction
2. Toward AI for breast cancer diagnosis
3. Conclusion
Chapter 3. Prediction of breast cancer diagnosis using random forest classifier
1. Introduction
2. Data set used
3. Several breast cancer risk factors
4. Various machine learning algorithms
5. Case study
6. Experimental results and discussions
7. Conclusion
Chapter 4. Medical image analysis of masses in mammography using deep learning model for early diagnosis of cancer tissues
1. Introduction
2. Related work
3. Proposed methodology
4. Performance analysis
5. Experimental results and discussions
6. Conclusion
Chapter 5. A framework for breast cancer diagnostics based on MobileNetV2 and LSTM-based deep learning
1. Introduction
2. Related work
3. Deep learning framework for breast cancer diagnosis
4. Experimental results and discussion
5. Conclusion
Chapter 6. Autoencoder-based dimensionality reduction in 3D breast images for efficient classification with processing by deep learning architectures
1. Introduction
2. Related works
3. System model
4. Performance analysis
5. Conclusion
Chapter 7. Prognosis of breast cancer using machine learning classifiers
1. Introduction
2. Breast cancer
3. Machine learning
4. Machine intelligence-aided mammography
5. Conclusion
Chapter 8. Breast cancer diagnosis through microcalcification
1. Introduction
2. Proposed method
3. Results and discussion
4. Conclusion
Chapter 9. Scrutinization of mammogram images using deep learning
1. Introduction
2. Literature review
3. Methodologies
4. Resources and procedures
5. Findings and analysis
6. Conclusions
7. Future work
Chapter 10. Computational techniques for analysis of breast cancer using molecular breast imaging
1. Introduction
2. Breast cancer
3. Statistics
4. Types of breast cancer
5. Screening methods
6. Image processing techniques
7. Image processing techniques
8. Classification techniques
9. Conclusions and future directions
Chapter 11. Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging
1. Introduction
2. Ultrasound and imaging techniques for staging of breast tumor
3. Machine learning techniques incorporated with ultrasound imaging
4. Deep learning techniques and ultrasound imaging
5. Comparison of popular AI methods employed for various image modalities
6. Limitations of ML and DL in imaging techniques
7. Open research problems and future trends
8. Conclusion
Chapter 12. Efficient transfer learning techniques for breast cancer histopathological image classification
1. Introduction
2. Related works
3. Methodology
4. Implementation
5. Results and discussions
6. Conclusion
Chapter 13. Classification of breast cancer histopathological images based on shape and texture attributes with ensemble machine learning methods
1. Introduction
2. Literature review
3. Methodology formulation
4. Results and discussion
5. Conclusion
Chapter 14. An automatic level set segmentation of breast tumor from mammogram images using optimized fuzzy c-means clustering
1. Introduction to mammogram image segmentation
2. Literature review on mammogram image segmentation
3. Proposed optimized fuzzy c-means clustering using level set method for breast tumor segmentation
4. Simulation results and discussions
5. Conclusion
Index

Life Sciences

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Social Sciences & Humanities

Health

Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images

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