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Deep Learning Techniques for Biomedical and Health Informatics

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Cover of 'Deep Learning Techniques for Biomedical and Health Informatics'

Table of Contents

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    Book Overview
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    Chapter 1 MedNLU: Natural Language Understander for Medical Texts
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    Chapter 2 Deep Learning Based Biomedical Named Entity Recognition Systems
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    Chapter 3 Disambiguation Model for Bio-Medical Named Entity Recognition
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    Chapter 4 Applications of Deep Learning in Healthcare and Biomedicine
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    Chapter 5 Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcare
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    Chapter 6 Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informatics
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    Chapter 7 Deep Learning and Explainable AI in Healthcare Using EHR
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    Chapter 8 Deep Learning for Analysis of Electronic Health Records (EHR)
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    Chapter 9 Application of Deep Architecture in Bioinformatics
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    Chapter 10 Intelligent, Secure Big Health Data Management Using Deep Learning and Blockchain Technology: An Overview
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    Chapter 11 Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizers
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    Chapter 12 Deep Reinforcement Learning Based Personalized Health Recommendations
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    Chapter 13 Using Deep Learning Based Natural Language Processing Techniques for Clinical Decision-Making with EHRs
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    Chapter 14 Diabetes Detection Using ECG Signals: An Overview
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    Chapter 15 Deep Learning and the Future of Biomedical Image Analysis
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    Chapter 16 Automated Brain Tumor Segmentation in MRI Images Using Deep Learning: Overview, Challenges and Future
Attention for Chapter 15: Deep Learning and the Future of Biomedical Image Analysis
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Citations

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55 Dimensions

Readers on

mendeley
18 Mendeley
Summary Dimensions citations
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Chapter title
Deep Learning and the Future of Biomedical Image Analysis
Chapter number 15
Book title
Deep Learning Techniques for Biomedical and Health Informatics
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-33966-1_15
Book ISBNs
978-3-03-033965-4, 978-3-03-033966-1
Authors

Monika Jyotiyana, Nishtha Kesswani, Jyotiyana, Monika, Kesswani, Nishtha

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 22%
Researcher 2 11%
Student > Ph. D. Student 2 11%
Student > Bachelor 1 6%
Other 1 6%
Other 1 6%
Unknown 7 39%
Readers by discipline Count As %
Engineering 3 17%
Computer Science 3 17%
Design 2 11%
Business, Management and Accounting 1 6%
Medicine and Dentistry 1 6%
Other 1 6%
Unknown 7 39%

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