Deep Learning in Biomedical and Health Informatics: Current Applications and Possibilities (Emerging Trends in Biomedical Technologies and Health informatics)

225 Pages·2021·30.701 MB·English

by  M. A. Jabbar (editor), Ajith Abraham (editor), Onur Dogan (editor), Ana Maria Madureira (editor), Sanju Tiwari (editor)

Deep Learning in
Biomedical and
Health Informatics
Emerging Trends in Biomedical Technologies and Health
Informatics Series
Series Editors:
Subhendu Kumar Pani
Orissa Engineering College, Bhubaneswar, Orissa, India
Sujata Dash
North Orissa University, Baripada, India
Sunil Vadera
University of Salford, Salford, UK
Everyday Technologies in Healthcare
Chhabi Rani Panigrahi, Bibudhendu Pati, Mamata Rath, Rajkumar Buyya
Biomedical Signal Processing for Healthcare Applications
Varun Bajaj, G R Sinha, Chinmay Chakraborty
Deep Learning in Biomedical and Health Informatics
MA. Jabbar, Ajith Abraham, Onur Dogan, Ana Madureira, Sanju Tiwari
For more information about this series, please visit: https://www.routledge.com/
Emerging- Trends- in- Biomedical- Technologies- and- Health- informatics- series/book-
series/ETBTHI
Deep Learning in
Biomedical and
Health Informatics
Current Applications and Possibilities
Edited by M.A. Jabbar, Ajith Abraham,
Onur Dogan, Ana Madureira and
Sanju Tiwari
MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks
does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of
MATLAB® software or related products does not constitute endorsement or sponsorship by The
MathWorks of a particular pedagogical approach or particular use of the MATLAB® software.
First edition published 2022
by CRC Press
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© 2022 selection and editorial matter, M.A. Jabbar, Ajith Abraham, Onur Dogan, Ana Madureira, Sanju
Tiwari; individual chapters, the contributors
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Library of Congress Cataloging‑in‑Publication Data
Names: Jabbar, M., editor.
Title: Deep learning in biomedical and health informatics / edited by M. Jabbar, Ajith Abraham,
Onur Dogan, Anu Madureira, Sanju Tiwari.
Description: First edition. | Boca Raton, FL : CRC Press, 2022. | Series:
Emerging trends in biomedical technologies and health informatics |
Includes bibliographical references and index. | Summary: “This book provides a proficient guide on the
relationship between AI and healthcare and how AI is changing all aspects of the health care industry.
It also covers how deep learning will help in diagnosis and prediction of disease spread”-- Provided by
publisher.
Identifiers: LCCN 2021010428 (print) | LCCN 2021010429 (ebook) | ISBN 9780367726041 (hbk) |
ISBN 9780367751548 (pbk) | ISBN 9781003161233 (ebk)
Subjects: LCSH: Diagnostic imaging--Data processing. | Artificial intelligence. |
Medical informatics--Medical applications. | Bioinformatics.
Classification: LCC RC78.7.D53 D434 2022 (print) | LCC RC78.7.D53 (ebook)
| DDC 616.07/54--dc23
LC record available at https://lccn.loc.gov/2021010428
LC ebook record available at https://lccn.loc.gov/2021010429
ISBN: 978- 0- 367- 75154- 8 (pbk)
ISBN: 978- 0- 367- 72604- 1 (hbk)
ISBN: 978- 1- 003- 16123- 3 (ebk)
DOI: 10.1201/9781003161233
Typeset in Times
by SPi Technologies India Pvt Ltd (Straive)
Contents
Tables .......................................................................................................................vii
Figures .......................................................................................................................ix
Abbreviations ............................................................................................................xi
Preface ......................................................................................................................xv
Acknowledgments ..................................................................................................xvii
Notes on the Editors ................................................................................................xix
Contributors ............................................................................................................xxi
Chapter 1 Foundations of Deep Learning and Its Applications to
Health Informatics ................................................................................1
Syed Saba Raoof, M.A. Jabbar, and Sanju Tiwari
Chapter 2 Deep Knowledge Mining of Complete HIV Genome
Sequences in Selected African Cohorts..............................................29
Moses Effiong Ekpenyong, Mercy E. Edoho, Ifiok J. Udo, and
Geoffery Joseph
Chapter 3 Review of Machine Learning Approach for Drug
Development Process .........................................................................53
Devottam Gaurav, Fernando Ortiz Rodriguez, Sanju Tiwari,
and M.A. Jabbar
Chapter 4 A Detailed Comparison of Deep Neural Networks for
Diagnosis of COVID- 19 .....................................................................79
M.B. Bicer, Onur Dogan, and O.F. Gurcan
Chapter 5 Deep Learning in BioMedical Applications: Detection of
Lung Disease with Convolutional Neural Networks ..........................97
Emre Olmez, Orhan Er, and Abdulkadir Hiziroglu
Chapter 6 Deep Learning Methods for Diagnosis of Covid- 19 Using
Radiology Images and Genome Sequences: Challenges
and Limitations .................................................................................117
Hilal Arslan and Hasan Arslan
v
vi Contents
Chapter 7 Applications of Lifetime Modeling with Competing Risks in
Biomedical Sciences.........................................................................137
N. Chandra and H. Rehman
Chapter 8 PeNLP Parser: An Extraction and Visualization Tool for
Precise Maternal, Neonatal and Child Healthcare
Geo- locations from Unstructured Data ............................................157
Patience Usoro Usip, Moses Effiong Ekpenyong,
Funebi Francis Ijebu, Kommomo Jacob Usang, and Ifiok J. Udo
Chapter 9 Recent Trends in Deep learning, Challenges and Opportunities ......183
S. Kannadhasan, R. Nagarajan, and M. Shanmuganantham
Index ......................................................................................................................199
Tables
1.1 History of Deep Learning ................................................................................4
1.2 DL Models Used for Various Applications of Healthcare .............................11
1.3 DL Methods Used in Disease Prediction .......................................................18
2.1 Cognitive Link Map of Isolate Clusters .........................................................43
2.2 DNN Classification Performance for Various Activation Functions ..............47
3.1 Tools/Software ...............................................................................................67
3.2 Database .........................................................................................................68
4.1 Summary of Related Works ...........................................................................82
4.2 Comparison Results .......................................................................................83
4.3 Deep Learning Models and Properties ...........................................................86
4.4 Performance Metrics for Classifiers ...............................................................89
5.1 Results Obtained by Dividing the Data Set Randomly by 30%
Testing and 70% Training ............................................................................111
5.2 Results of the Study by Reducing the Size and Number of
the Convolution Filters .................................................................................112
5.3 The Classification Accuracies Obtained by CNN and PNN
Structures for Lung Diseases .......................................................................113
6.1 Comparison of Existing Deep Learning Studies in COVID-19 Datasets ....129
7.1 Estimates of Regression Parameters with Hazard Ratio (HR)
Using CSH Approach ...................................................................................151
7.2 Estimates of Regression Parameters with Hazard Ratio (HR)
Using Fine-Gray SDH Approach .................................................................152
8.1 Maternal Locational Data with Ailment from 2014 to 2020 ........................167
8.2 Neonatal Locational Data with Ailment from 2014 to 2020 ........................168
8.3 Child Locational Data with Ailment from 2014 to 2020 .............................169
8.4 Performance Evaluation of our PeNLP for Unstructured Maternal Data ....177
8.5 Performance Evaluation of Our PeNLP for Unstructured Neonatal Data ...177
8.6 Performance Evaluation of Our PeNLP for Unstructured Child Data .........177
9.1 Number of Papers Published in Deep Learning Network ............................192
vii

Figures
1.1 Representation of a NN ....................................................................................5
1.2 Architecture of a FFNN ffn ..............................................................................6
1.3 CNN architecture .............................................................................................6
1.4 RNN architecture .............................................................................................7
1.5 LSTM representation .......................................................................................7
1.6 AE architecture ................................................................................................8
1.7 Boltzmann machine .........................................................................................9
1.8 GAN architecture .............................................................................................9
1.9 Challenges of DL in healthcare ......................................................................19
2.1 Proposed implementation workflow ..............................................................40
2.2 SOM component planes for excavated HIV- 1 genome sequences .................42
2.3 Clustering nucleotide transition frequency by country ..................................44
2.4 Clustering nucleotide mutation frequency by country ...................................45
2.5 Proposed DNN architecture ...........................................................................46
2.6 Confusion matrices of various activation functions .......................................47
3.1 Applications of drug discovery ......................................................................54
3.2 Overview of drug discovery ...........................................................................58
3.3 Stages of drug discovery ................................................................................59
3.4 Drug discovery ...............................................................................................60
3.5 Pre- clinical studies .........................................................................................61
3.6 Clinical trials ..................................................................................................62
3.7 FDA reviews ..................................................................................................63
3.8 Post- market reviews .......................................................................................63
3.9 GANs .............................................................................................................65
3.10 RCT ................................................................................................................69
3.11 ACT ................................................................................................................69
4.1 The proposed model .......................................................................................83
4.2 Classification accuracy of the pre- trained models .........................................87
4.3 Classification accuracy of the pre- trained models with classifiers ................88
4.4 Chest X- rays for (a) COVID- 19, (b) normal and
(c) viral pneumonia classes ............................................................................90
4.5 Density distribution of areas in detecting classes ..........................................91
5.1 Deep learning method for lung disease ........................................................100
5.2 Comparison of CNN and ANN architectures ...............................................103
5.3 Feature extraction and classification layers in the CNN architectures.........104
5.4 Convolution process .....................................................................................105
5.5 ReLU process ...............................................................................................105
5.6 Max pooling process with 2 × 2 filter ..........................................................106
5.7 Sample max- pooling process on MR images ...............................................106
5.8 Gradient descent algorithm ..........................................................................109
5.9 CNN architecture and parameters designed for lung diseases .....................110
5.10 The model with the highest success rate ......................................................112
ix