Machine Learning in Medicine

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

• Preface. Acknowledgements. Editors. Contributors. Chapter 1 Another Set of Eyes in Anesthesiology. Chapter 2 Dermatological Machine Learning Clinical Decision Support System. Chapter 3 Vision and AI. Chapter 4 Thermal Dose Modeling for Thermal Ablative Cancer Treatments by Cellular Neural Networks. Chapter 5 Ensembles of Convolutional Neural Networks with Different Activation Functions for Small to Medium-Sized Biomedical Datasets. Chapter 6 Analysis of Structural MRI Data for Epilepsy Diagnosis Using Machine Learning Techniques. Chapter 7 Artificial Intelligence-Powered Ultrasound for Diagnosis and Improving Clinical Workflow. Chapter 8 Machine Learning for E/MEG-Based Identification of Alzheimer’s Disease. Chapter 9 Some Practical Challenges with Possible Solutions for Machine Learning in Medical Imaging. Chapter 10 Detection of Abnormal Activities Stemming from Cognitive Decline Using Deep Learning. Chapter 11 Classification of Left Ventricular Hypertrophy and NAFLD through Decision Tree Algorithm. Chapter 12 The Cutting Edge of Surgical Practice: Applications of Machine Learning to Neurosurgery. Chapter 13 A Novel MRA-Based Framework for the Detection of Cerebrovascular Changes and Correlation to Blood Pressure. Chapter 14 Early Classification of Renal Rejection Types: A Deep Learning Approach. Index.