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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Table of Contents
1. Mammogram Data Analysis: Trends, Challenges, and Future Directions2. AI in Breast Imaging: Applications, Challenges and Future Research
3. Prediction of Breast Cancer Diagnosis Using a Random Forest Classifier
4. Medical Image Analysis of masses in Mammography using Deep Learning model for Earlier Diagnosis of Cancer Tissues
5. A framwork for breast cancer diagnostics based on MobileNetV2 and LSTM-based deep learning
6. Autoencoder based dimensionality reduction in 3D breast images for efficient classification with processing by deep learning architectures
7. Prognosis of breast cancer using machine learning classifiers
8. Breast cancer diagnosis through microcalcification
9. Scutinization of Mammogram Images using deep learning
10. Computational Techniques for Analysis of Breast Cancer Using Molecular Breast Imaging
11. Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging
12. Efficient Transfer Learning Techniques for Breast Cancer Histopathological Image Classification
13. Classification of breast cancer histopathological images based on shape and texture attributes with ensemble machine learning methods
14. An automatic level set segmentation of breast Tumor from mammogram images using optimized Fuzzy c-means clustering
Authors
D. Jude Hemanth Professor, ECE Department, Karunya Institute of Technology and Sciences, Coimbatore, India. Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of "Visiting Professor� in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the "Research Scientist� of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain.Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.
