Machine Learning in Medicine

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.

Auteur

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

Contenu

Dedication

Preface

Acknowledgements

Editor Biographies

List of 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

Pushkar Aggarwal

Chapter 4: Thermal dose modelling for thermal ablative cancer treatments by cellular neural networks

Jinao Zhang, Wa Cheung, Stuart K. Roberts, and Sunita Chauhan.

Chapter 5: Ensembles of Convolutional Neural Networks with Different Activation Functions for Small to Medium Size Biomedical Datasets

Filippo Berno, Loris Nanni, Gianluca Maguolo, Sheryl Brahnam

Chapter 6: Analysis of Structural MRI Data for Epilepsy Diagnosis Using Machine Learning Techniques

Seyedmohammad Shams, Esmaeil Davoodi-Bojd, and Hamid Soltanian-Zadeh.

Chapter 7: Artificial Intelligence (AI) Powered Ultrasound for Diagnosis and Improving Clinical Workflow

Zeynettin Akkus

Chapter 8: Machine Learning for M/EEG-based Identification of Alzheimer's Disease

Su Yang, Jose Sanchez-Bornot, KongFatt Wong-Lin, Girijesh Prasad

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: Detecting of Abnormal Activities stemming from Cognitive Decline using Deep Learning

Damla Arifoglu, Abdelhamid Bouchachia

Chapter 11: Classification of Left Ventricular Hypertrophy and NAFLD Through Decision Tree Algorithm.

Arnulfo González-Cantú, Maria Elena Romero-Ibarguengoitia, 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, Michael G. Fehlings

Chapter 13: A Novel MRA-Based Framework for the Detection of Cerebrovascular Changes and Correlation to Blood Pressure

Ingy El-Torgoman, Ali Mahmoud, Ahmed Shalaby, Mohammed Ghazal, Guruprasad Giridharan and Ayman El-Baz, Jasjit S. Suri

Chapter 14: Early Classification of Renal Rejection Types: A Deep Learning Approach

Mohamed Shehata, Fahmi Khalifa, Ahmed Soliman, Mohamed Abou El-Ghar, Shams Shaker, Ahmed Shalaby, Maryam El-Baz, Ali Mahmoud, Mohammed Ghazal, Amy C. Dwyer, Ayman El-Baz, Jasjit S. Suri

Index