Overview
- Highlights recent advanced applications of Deep Learning for diagnosing cancer
- Discusses relevant solutions for medical diagnosis using techniques such as CNN, LSTM, and Autoencoder Networks
- Offers a valuable reference guide for practitioners, students, and researchers alike, supporting them in cancer diagnosis
Part of the book series: Studies in Computational Intelligence (SCI, volume 908)
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Table of contents (16 chapters)
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Front Matter
Keywords
About this book
Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
Editors and Affiliations
About the editors
Utku Kose received his Ph. D. degree in 2017 from Selcuk University, Turkey in the field of computer engineering. Currently, he is an Associate Professor in Suleyman Demirel University, Turkey. He has more than 100 publications to his credit. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, the chaos theory, distance education, e-learning, computer education, and computer science.
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 Dept., Al-Balqa Applied University, Jordan. His research focuses on Elliptic curves cryptography and cryptosystems, 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.
Bibliographic Information
Book Title: Deep Learning for Cancer Diagnosis
Editors: Utku Kose, Jafar Alzubi
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-981-15-6321-8
Publisher: Springer Singapore
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Hardcover ISBN: 978-981-15-6320-1Published: 13 September 2020
Softcover ISBN: 978-981-15-6323-2Published: 14 September 2021
eBook ISBN: 978-981-15-6321-8Published: 12 September 2020
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIX, 300
Number of Illustrations: 31 b/w illustrations, 87 illustrations in colour
Topics: Computational Intelligence, Machine Learning, Cancer Research, Health Informatics, Computer Imaging, Vision, Pattern Recognition and Graphics, Signal, Image and Speech Processing