Overview
- Editors:
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Mourad Elloumi
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Computing and Information Technology, The University of Bisha, Bisha, Saudi Arabia
Surveys the most recent techniques and approaches in the field of Deep Learning and biomedical data analysis
Offers enough fundamental and technical information on Deep Learning techniques, approaches and the related problems
Presents the results of the latest investigations in the field of Deep Learning for biomedical data analysis
Tries to find a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to Deep Learning
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Table of contents (13 chapters)
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Deep Learning for Biomedical DataAnalysis
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- Samson Anosh Babu Parisapogu, Chandra Sekhara Rao Annavarapu, Mourad Elloumi
Pages 3-26
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- Domenico Amato, Mattia Antonino Di Gangi, Antonino Fiannaca, Laura La Paglia, Massimo La Rosa, Giosué Lo Bosco et al.
Pages 27-59
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- Mirto Musci, Marco Piastra
Pages 81-98
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Deep Learning for Biomedical Image Analysis
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- Jitesh Pradhan, Arup Kumar Pal, Haider Banka
Pages 101-128
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- Ashif Sheikh, Jitesh Pradhan, Arpit Dhuriya, Arup Kumar Pal
Pages 129-152
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- Cédric Wemmert, Jonathan Weber, Friedrich Feuerhake, Germain Forestier
Pages 153-169
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- Ryad Zemouri, Daniel Racoceanu
Pages 171-196
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- Daniel A. Greenfield, Germán González, Conor L. Evans
Pages 197-236
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Deep Learning for Medical Diagnostics
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Front Matter
Pages 237-237
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- Ebenezer Jangam, Chandra Sekhara Rao Annavarapu, Mourad Elloumi
Pages 239-254
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- Abedalrhman Alkhateeb, Ashraf Abou Tabl, Luis Rueda
Pages 255-271
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- Hasan Zafari, Leanne Kosowan, Jason T. Lam, William Peeler, Mohammad Gasmallah, Farhana Zulkernine et al.
Pages 273-310
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- Asim Waqas, Dimah Dera, Ghulam Rasool, Nidhal Carla Bouaynaya, Hassan M. Fathallah-Shaykh
Pages 311-350
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Back Matter
Pages 351-359
About this book
This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis.
The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
Editors and Affiliations
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Computing and Information Technology, The University of Bisha, Bisha, Saudi Arabia
Mourad Elloumi
About the editor
Mourad Elloumi received an Undergraduate Degree in Mathematics and Physics in 1984, and a Master's Degree in Computer Engineering in 1988, from the Faculty of Sciences of Tunis, Tunisia. He also received a Master's Degree in Computer Science in 1989, and a PhD Degree in Computer Science in 1994, from the University of Aix-Marseilles III, France. Then, he received a Habilitation for conducting research in Computer Science in 2003, from the National School of Computer Science, Tunis, Tunisia. He is currently a Full Professor in Computer Science, Faculty of Computing and Information Technology, The University of Bisha, Saudi Arabia, and Head of the BioInformatics Group (BIG) of the Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), National Higher School of Engineers of Tunis (ENSIT), University of Tunis, Tunisia. Professor Mourad Elloumi is the author/co-author of more than 70 publications in international journals, books and conference proceedings. He was a Guest Editor of a special issue on biological knowledge discovery and data mining, Knowledge Based Systems Journal (Elsevier 2002), a Guest Editor of a special issue on pattern finding in Computational Molecular Biology, Recent Patents on DNA and Gene Sequence Journal (Bentham Science 2012), a Co-Editor of the proceedings of two international conferences and a Co-Editor of four books, respectively, on Algorithms in Computational Molecular Biology (Wiley 2011), Biological Knowledge Discovery (Wiley 2014), Pattern Recognition in Computational Molecular Biology (Wiley 2015), and Algorithms for Next-Generation Sequencing Data (Springer 2017). His research interests are Algorithmics, Computational Molecular Biology, Knowledge Discovery and Data Mining, and Deep Learning.
