Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems.
New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different activation functions for small- to medium-size biomedical datasets, detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments, dermatological machine learning clinical decision support systems, artificial intelligence-powered ultrasound for diagnosis, practical challenges with possible solutions for machine learning in medical imaging, epilepsy diagnosis from structural MRI, Alzheimer's disease diagnosis, classification of left ventricular hypertrophy, and intelligent medical language understanding.
This book will help to advance scientific research within the broad field of machine learning in the medical field. It focuses on major trends and challenges in this area and presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.
Preface
Acknowledgements
Editors
Contributors
Chapter 1 Another Set of Eyes in Anesthesiology
Pushkar Aggarwal
Chapter 2 Dermatological Machine Learning Clinical Decision Support System
Pushkar Aggarwal
Chapter 3 Vision and AI
Mohini Bindal and Pushkar Aggarwal
Chapter 4 Thermal Dose Modeling for Thermal Ablative Cancer Treatments by Cellular Neural Networks
Jinao Zhang, Sunita Chauhan, Wa Cheung, and Stuart K. Roberts
Chapter 5 Ensembles of Convolutional Neural Networks with Different Activation Functions for Small to Medium-Sized Biomedical Datasets
Filippo Berno, Loris Nanni, Gianluca Maguolo, and Sheryl Brahnam
Chapter 6 Analysis of Structural MRI Data for Epilepsy Diagnosis Using Machine Learning Techniques
Seyedmohamm ad Shams, Esmaeil Davoodi-Bojd, and Hamid Soltanian-Zadeh
Chapter 7 Artificial Intelligence-Powered Ultrasound for Diagnosis and Improving Clinical Workflow
Zeynettin Akkus
Chapter 8 Machine Learning for E/MEG-Based Identification of Alzheimer’s Disease
Su Yang, Girijesh Prasad, KongFatt Wong-Lin, and Jose Sanchez-Bornot
Chapter 9 Some Practical Challenges with Possible Solutions for Machine Learning in Medical Imaging
Naimul Khan, Nabila Abraham, Anika Tabassum, and Marcia Hon
Chapter 10 Detection of Abnormal Activities Stemming from Cognitive Decline Using Deep Learning
Damla Arifoglu and Abdelhamid Bouchachia
Chapter 11 Classification of Left Ventricular Hypertrophy and NAFLD through Decision Tree Algorithm
Arnulfo González-Cantú, Maria Elena Romero-Ibarguengoitia, and Baidya Nath Saha
Chapter 12 The Cutting Edge of Surgical Practice: Applications of Machine Learning to Neurosurgery
Omar Khan, Jetan H. Badhiwala, Muhammad Ali Akbar, and Michael G. Fehlings
Chapter 13 A Novel MRA-Based Framework for the Detection of Cerebrovascular Changes and Correlation to Blood Pressure
Ingy El-Torgoman, Ahmed Soliman, Ali Mahmoud, Ahmed Shalaby, Mohamm ed Ghazal, Guruprasad Giridharan, Jasjit S. Suri, and Ayman El-Baz
Chapter 14 Early Classification of Renal Rejection Types: A Deep Learning Approach
Mohamed Shehata, Fahmi Khalifa, Ahmed Soliman, Shams Shaker, Ahmed Shalaby, Maryam El-Baz, Ali Mahmoud, Mohamed Abou El-Ghar, Mohammed Ghazal, Amy C. Dwyer, Jasjit S. Suri, and Ayman El-Baz
Index
Biography
Ayman El-Baz is a Distinguished Professor at University of Louisville, Kentucky, United States and University of Louisville at AlAlamein International University (UofL-AIU), New Alamein City, Egypt. Dr. El-Baz earned his B.Sc. and M.Sc. degrees in electrical engineering in 1997 and 2001, respectively. He earned his Ph.D. in electrical engineering from the University of Louisville in 2006. Dr. El-Baz was named as a Fellow for Coulter, AIMBE and NAI for his contributions to the field of biomedical translational research. Dr. El-Baz has almost two decades of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 500 technical articles (155 journals, 44 books, 85 book chapters, 255 refereed-conference papers, 196 abstracts, and 36 US patents and Disclosures).
Jasjit S. Suri is an innovator, scientist, visionary, industrialist and an internationally known world leader in biomedical engineering. Dr. Suri has spent over 25 years in the field of biomedical engineering/devices and its management. He received his Ph.D. from the University of Washington, Seattle and his Business Management Sciences degree from Weatherhead, Case Western Reserve University, Cleveland, Ohio. Dr. Suri was crowned with President’s Gold medal in 1980 and made Fellow of the American Institute of Medical and Biological Engineering for his outstanding contributions. In 2018, he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management
