Deep Learning for Medical Decision Support Systems.pdf

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Foreword by Dr. Deepak Gupta

Foreword by Dr. Jose Antonio Marmolejo-Saucedo and Dr. Igor Litvinchev

Preface

About This Book

Contents

utkukose@sdu.edu.tr

bogdan@info.uaic.ro

1 Artificial Intelligence and Decision Support Systems

1.1 Artificial Intelligence and Intelligent Systems

1.1.1 Areas of Artificial Intelligence

1.1.2 Intelligent Systems

1.2 Decision Support Systems

1.2.1 Decision Support Systems for Medical and Deep Learning

1.3 Summary

1.4 Further Learning

References

2 Deep Learning Architectures for Medical Diagnosis

2.1 Deep Learning for Medical Diagnosis

2.2 Deep Learning Architectures

2.2.1 Convolutional Neural Networks

2.2.2 Recurrent Neural Networks

2.2.3 Autoencoder Neural Network

2.2.4 Deep Neural Networks

2.2.5 Deep Belief Network

2.2.6 Deep Reinforcement Learning

2.2.7 Other Deep Learning Architectures

2.3 Summary

2.4 Further Learning

References

3 A Brief View on Medical Diagnosis Applications with Deep Learning

3.1 Convolutional Neural Networks Applications

3.2 Recurrent Neural Networks Applications

3.3 Autoencoder Neural Network Applications

3.4 Deep Neural Network Applications

3.5 Deep Belief Network Applications

3.6 Deep Reinforcement Learning Applications

3.7 Applications with Other Deep Learning Architectures

3.8 Summary

3.9 Further Learning

References

4 Diagnosing Diabetic Retinopathy by Using a Blood Vessel Extraction Technique and a Convolutional Neural Network

4.1 Related Works

4.2 Materials and Methods

4.2.1 Messidor Dataset

4.2.2 Image Processing

4.2.3 Classification with Convolutional Neural Network

4.2.4 Evaluation of Performance

4.3 Diagnosis Application

4.4 Results and Discussion

4.5 Summary

4.6 Further Learning

References

5 Diagnosing Parkinson by Using Deep Autoencoder Neural Network

5.1 Related Works

5.2 Materials and Methods

5.2.1 The Dataset of Oxford Parkinson’s Disease Diagnosis

5.2.2 Classification

5.2.3 Evaluating the Performance

5.3 Classification Application

5.4 Results and Discussion

5.5 Summary

5.6 Further Learning

References

6 A Practical Method for Early Diagnosis of Heart Diseases via Deep Neural Network

6.1 Fundamentals

6.1.1 Cleveland Heart Disease Data Set

6.1.2 Autoencoder Neural Network

6.1.3 Performance Evaluation

6.2 Early Diagnosis of Heart Diseases

6.3 Results and Discussion

6.4 Summary

6.5 Further Learning

References

7 A Hybrid Medical Diagnosis Approach with Swarm Intelligence Supported Autoencoder Based Recurrent Neural Network System

7.1 Related Work

7.2 Swarm Intelligence and Autoencoder Based Recurrent Neural Network for Medical Diagnosis

7.2.1 Autoencoder Based Recurrent Neural Network (ARNN)

7.2.2 Swarm Intelligence and Intelligent Optimization in the SIARNN

7.3 Design of the SIARNN

7.4 Applications and Evaluation

7.4.1 Medical Diagnosis Applications with SIARNN

7.4.2 Comparative Evaluation

7.5 Results and Future Work

7.6 Summary

7.7 Further Learning

References

8 Psychological Personal Support System with Long Short Term Memory and Facial Expressions Recognition Approach

8.1 Background

8.1.1 Facial Recognition and Facial Expressions

8.1.2 Long Short Term Memory

8.2 The Model of the Psychological Personal Support System

8.2.1 Infrastructure for Facial Expressions

8.2.2 Long Short Term Memory Based Approach for Psychological Testing Process

8.2.3 API Mechanism

8.3 Evaluation

8.4 Results and Discussion

8.5 Summary

8.6 Further Learning

References

9 Diagnosing of Diabetic Retinopathy with Image Dehazing and Capsule Network

9.1 Materials and Method

9.1.1 Kaggle Diabetic Retinopathy Database for Diagnosis

9.1.2 Image Processing

9.1.3 Classification

9.1.4 Evaluation of the Diagnosis

9.2 Application and Evaluation

9.3 Results

9.4 Summary

9.5 Further Learning

References

10 Future of Medical Decision Support Systems

10.1 Internet of Health Things and Wearable Technologies

10.2 Robotics

10.3 Information and Drug Discovery

10.4 Rare Disease and Cancer Diagnosis

10.5 COVID-19 and Pandemics Control

10.6 Summary

10.7 Further Learning

References

Studies in Computational Intelligence 909 Utku Kose Omer Deperlioglu Jafar Alzubi Bogdan Patrut Deep Learning for Medical Decision Support Systems

Studies in Computational Intelligence Volume 909 Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Computational Intelligence” (SCI) publishes new develop- ments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the ﬁelds of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artiﬁcial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink. this series are submitted to indexing to Web of Science, More information about this series at http://www.springer.com/series/7092

Utku Kose Omer Deperlioglu Jafar Alzubi Bogdan Patrut Deep Learning for Medical Decision Support Systems 123

Utku Kose Department of Computer Engineering Süleyman Demirel University Isparta, Turkey Omer Deperlioglu Department of Computer Technologies Afyon Kocatepe University Afyonkarahisar, Turkey Jafar Alzubi Faculty of Engineering Al-Balqa Applied University Al-Salt, Jordan Bogdan Patrut Faculty of Computer Science Alexandru Ioan Cuza University of Iasi Iasi, Romania ISSN 1860-949X Studies in Computational Intelligence ISBN 978-981-15-6324-9 https://doi.org/10.1007/978-981-15-6325-6 ISBN 978-981-15-6325-6 (eBook) ISSN 1860-9503 (electronic) © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. trademarks, service marks, etc. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword by Dr. Deepak Gupta intelligence is brieﬂy an engineering perspective for designing and Artiﬁcial developing ﬂexible and robust solutions, which can be widely applied to real-world-related problems. As associated with many remarkable technologies such as computer, communication, and electronics, outputs of the ﬁeld of artiﬁcial intelligence has already been seen in different areas at the start of the twenty-ﬁrst century. Now, it can be seen that the use of artiﬁcial intelligence is in its way to be a common thing used widely by people during daily life. That is because innovative devices gained great rise as a result of more increases in need for using, changing and changing the information even instantly worldwide. By the way, effective advantages of artiﬁcial intelligence-based methods and techniques for automated decision-making have been taking researchers’ interests for a very long time, as taking us back to even starting times of the artiﬁcial intelligence. It seems that the decision support will be one of the most critical roles of artiﬁcial intelligence-based systems of the future because more intense digital data requires automated analysis and evaluation phases as it may take time and be very difﬁcult for humans to make that even without errors. Because of that we can see active uses of decision support systems in both natural and social sciences, and the ﬁeld of medical is among them as it includes decision-making phases during symptom analyzes, diagnosis, and treatment. It is a pleasure for me to write a foreword for this book as it includes recent achievements and skills–knowledge combination in the context of deep learning and medical decision support systems. As the current, more advanced form of the machine learning, the deep learning is a wide collection of neural networks cur- rently, and it is applied with improved success rates in problems of medical. Thanks to its relation with especially data processing techniques; it has been even easier and faster to get better, more accurate and effective outcomes for the medical applica- tions. In that context, this book takes readers from brief introduction to the essential concepts, and then goes with diagnosis-related different applications including uses of different deep learning techniques as well as different diseases in the target. It is good to see that all subjects covered in the book are explained in enough technical details and giving further information about what can be learned and how to v

vi Foreword by Dr. Deepak Gupta proceed next. The chapters also generally get combinations of deep learning and data processing to ensure automated diagnosis so that medical decision supports accordingly. I suggest the readers to have a start from Chap. 1, and then read the Chaps. 2–9 for better learning about skills for performing alternative research. I liked also that the ﬁnal Chap. 10 have a ﬁnal discussion about future perspectives for the future of medical decision support systems and also give a scenario for using current and future technologies for tracking and controlling pandemics, as it is very critical because the world and the existence of the human is nowadays under the attack by the COVID-19, which is a fatal virus type. I suggest the book to be used during the courses regarding the ﬁelds of computer science/engineering, medical and biomedical engineering, and also it will be a good reference for the data science-oriented courses, too. The level of technical details and the used language is all appropriate for the students at the level of B.Sc., M.Sc., Ph.D., and also post-docs. I take interests the readers to also suggestions made by the authors at the end of each chapter, in order to continue improving their knowledge. I would like to thank Dr. Utku Kose and his co-authors Dr. Omer Deperlioglu, Dr. Jafar Alzubi, and Dr. Bogdan Patrut for their valuable work and also wish the readers to have enjoyable learning as well as research experiences with the support of that book. Dr. Deepak Gupta Maharaja Agrasen Institute of Technology New Delhi, India e-mail: deepakgupta@mait.ac.in

Foreword by Dr. Jose Antonio Marmolejo-Saucedo and Dr. Igor Litvinchev Recent advances in information technologies result in evolution of decision support systems involving various techniques for analyses and handling big data. These systems are applied in a broad range of disciplines, e.g., in administration, engi- neering, and health systems. In the ﬁeld of medical informatics, designing and developing tools to support decision-making were highly motivated by advances in biometrics. Among dif- ferent applications in health systems, medical diagnostics is especially important. Diagnostics is often challenging since many signs and symptoms are hidden and nonspeciﬁc. To cope with this problem, a correlation of the information must be analyzed, combined with recognition and differentiation of patterns. Algorithms for data analysis are among various techniques used in diagnostic procedures. Among them, neural networks and deep learning approaches play an important role. In medical diagnostics, the deep learning frequently provides more robust results comparing with the artiﬁcial neural networks. The deep learning techniques were successfully used for cancer diagnostics. Many other ﬁelds of medicine are also open for high-level decision support systems that can diagnose and treat better than humans. In this book, different medical data handling techniques are used to develop medical decision support systems in the context of diagnosis perspective. The objective is to use all available information in the decision-making to improve the quality of medical care and to help less experienced doctors in diagnostics. Challenges and problems associated with implementations of medical decision support systems are discussed, as well as strategies for their development and validation. The book describes theoretical foundations used for developing decision support systems. Basic information on medical diagnostics in different situations is also presented. Finally, perspectives of medical decision support systems are dis- cussed and related with that of the progress in artiﬁcial intelligence, deep learning, and modern innovative technologies such as Internet of Health Things. vii

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