Deep Learning for Medical Decision Support Systems

ISBN-13: 9789811563270 / Angielski / Miękka / 2021 / 171 str.

Utku Kose; Omer Deperlioglu; Jafar Alzubi

Recenzje

"It covers several interesting applications of deep learning in medicine ... . the book can be a helpful addition to a researcher interested in a general overview of how deep learning can be applied to some medical decision systems." (Anita T. Layton, SIAM Review, Vol. 63 (4), December, 2021)

Spis treści

1.      Deep Learning for Innovative Medical Decision Support

2.      Deep Learning and Image Analysis for Medical Decision Support

3.      Deep Learning Oriented Systems for Medical Education

4.      Hybrid Deep Systems for Medical Education and Decision Support

5.      Deep Learning and Optimization for Medical Education and Decision Support

6.      Deep Learning and Multimedia for Medical Education and Decision Support

7.       Deep Learning and Traditional Methods for Medical Education and Decision Support

O autorze

UtkuKose received his PhD degree in Computer Engineeringfrom Selcuk University, Turkey, in 2017. Currently, he is an Associate Professor at SuleymanDemirel University, Turkey. With more than 100 publications to his credit, his research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science.

Omer Deperlioglu received his PhD in Computer Science from Gazi University, Turkey, in 2001. Currently, he is an Associate Professor of Computer Programming, Afyon Vocational School, Afyon Kocatepe University, Turkey. His research interests include various aspects of artificial intelligence applied to power electronics, biomedical and signal processing. 

Jafar Alzubi received his PhD in Advanced Telecommunications Engineering from Swansea University, UK, in 2012. He is currently an Associate Professor at the Computer Engineering Department, Al-Balqa Applied University, Jordan. His research focuses on elliptic curves cryptography and cryptosystems, and classifications and detection of web scams using Algebraic–Geometric theory in channel coding for wireless networks. He is currently working jointly with Wake Forest University, NC, USA, as a Visiting Associate Professor.

Bogdan Patrut received his two PhDs, respectively, from “AlexandruIoanCuza” University of Iasi, Romania (2007, in Accounting and Business Information Systems), and Babes-Bolyai University of Cluj-Napoca, Romania (2008, in Computer Science). Currently, he is a Lecturer at the Faculty of Computer Science, “AlexandruIoanCuza” University of Iasi. He is also the Director of EduSoft Ltd., Bacau, Romania. His research interests include multi-agent systems applied in accounting education, and computer science applied in thesocial and political sciences. He has published or edited over 25 books on programming, algorithms, artificial intelligence, interactive education, and social media, including Social Media and the New Academic Environment and Social Media in Higher Education.

Blurb

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. 

Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.