Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems.
- Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets
- Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis
- Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications
| Contributors |
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| About the editors |
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xiii | |
| Foreword |
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| Preface |
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xvii | |
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1 A deep learning approach for the prediction of heart attacks based on data analysis |
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2 A comparative study on fully convolutional networks--FCN-8, FCN-16, and FCN-32: A case of brain tumor |
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Iyyanki V. Murali Krishna |
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21 | (6) |
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27 | (4) |
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3 Deep learning applications for disease diagnosis |
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31 | (22) |
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32 | (1) |
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33 | (4) |
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37 | (2) |
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5 Current situation of deep learning in disease diagnosis |
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6 Advantages of deep learning in medical diagnosis |
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40 | (1) |
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41 | (6) |
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47 | (2) |
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9 Conclusion and future scope |
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49 | (4) |
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50 | (3) |
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4 An artificial intelligent cognitive approach for classification and recognition of white blood cells employing deep learning for medical applications |
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Ana Carolina Borges Monteiro |
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2 Cognitive computing concept |
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3 Neural networks concepts |
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4 Metaheuristic algorithm proposal |
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63 | (3) |
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6 Future research directions |
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67 | (4) |
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5 Deep learning on medical image analysis on COVID-19 x-ray dataset using an X-Net architecture |
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71 | (36) |
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72 | (2) |
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74 | (3) |
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3 Data set and image augmentation |
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4 Convolutional neural network architectures and proposed model |
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6 Detecting x-ray images through prediction |
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7 Conclusion and future scope |
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104 | (3) |
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6 Early prediction of heart disease using deep learning approach |
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107 | (2) |
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109 | (1) |
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4 Classification techniques and performance analysis |
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119 | (1) |
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120 | (3) |
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120 | (3) |
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7 Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system |
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124 | (1) |
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2 Machine learning: Regression models |
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3 Machine learning algorithms |
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141 | (6) |
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147 | (6) |
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Appendix 1 Models of FCN-8, FCN-16, and FCN-32 |
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8 Automatic detection of white matter hyperintensities via mask region-based convolutional neural networks using magnetic resonance images |
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158 | (8) |
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5 Discussion and conclusion |
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174 | (1) |
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175 | (6) |
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9 Diagnosing glaucoma with optic disk segmenting and deep learning from color retinal fundus images |
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181 | (16) |
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181 | (1) |
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182 | (3) |
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185 | (5) |
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190 | (2) |
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192 | (5) |
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10 An artificial intelligence framework to ensure a trade-off between sanitary and economic perspectives during the COVID-19 pandemic |
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1 Introduction to artificial intelligence methods employed to tackle the COVID-19 pandemic |
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3 General description of the trade-off model |
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4 Methods adapted to the field of COVID-19-related applications |
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5 Impacts of sanitary measures on the economy |
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11 Prediction of COVID-19 using machine learning techniques |
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R. S. M. Lakshmi Patibandla |
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3 Applications of AI, machine learning, and deep learning |
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4 Coronavirus disease-2019 prediction using machine learning |
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230 | (3) |
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| Index |
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Dr. Aditya Khamparia has expertise in teaching, entrepreneurship, and research and development of 11 years. He is presently working as Assistant Professor in Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received his Ph.D. degree from Lovely Professional University, Punjab, India in May 2018. He has completed his M. Tech. from VIT University, Vellore, Tamil Nadu, India and B. Tech. from RGPV, Bhopal, Madhya Pradesh, India. He has completed his PDF from UNIFOR, Brazil. He has published around 105 research papers along with book chapters including more than 25 papers in SCI indexed Journals with cumulative impact factor of above 100 to his credit. Additionally, he has authored and edited eleven books. Furthermore, he has served the research field as a Keynote Speaker/Session Chair/Reviewer/TPC member/Guest Editor and many more positions in various conferences and journals. His research interest include machine learning, deep learning for biomedical health informatics, educational technologies, and computer vision.
Dr. Utku Kose is an Associate Professor at Süleyman Demirel University, Turkey. He received his PhD from Selcuk University, Turkey, in the field of computer engineering. He has more than 100 publications to his credit, including Deep Learning for Medical Decision Support Systems, Springer; Artificial Intelligence Applications in Distance Education, IGI Global; Smart Applications with Advanced Machine Learning and Human-Centered Problem Design, Springer; Artificial Intelligence for Data-Driven Medical Diagnosis, DeGruyter; Computational Intelligence in Software Modeling, DeGruyter; Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; and Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press, among others. Dr. Kose is a Series Editor of the Biomedical and Robotics Healthcare series from Taylor & Francis/CRC Press. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science. Dr. Ashish Khanna has 16 years of expertise in teaching, entrepreneurship, and research and development. He received his PhD from the National Institute of Technology, Kurukshetra, India, and completed a post-doc degree at the National Institute of Telecommunications (Inatel), Brazil. He has published around 40 SCI-indexed papers in 'IEEE Transactions', and in other reputed journals by Springer, Elsevier, and Wiley, with a cumulative impact factor of above 100. He has published around 90 research articles in top SCI/Scopus journals, conferences, and book chapters. He is co-author or editor of numerous books, including 'Advanced Computational Techniques for Virtual Reality in Healthcare' (Springer), 'Intelligent Data Analysis: From Data Gathering to Data Comprehension' (Wiley), and 'Hybrid Computational Intelligence: Challenges and Applications' (Elsevier). His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning. He is one of the founders of Bhavya Publications and the Universal Innovator Lab, which is actively involved in research, innovation, conferences, start-up funding events, and workshops. He is currently working at the Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain. Dr. Valentina Emilia Balas is currently a Full Professor at the Department of Automatics and Applied Software at the Faculty of Engineering, Aurel Vlaicu University of Arad, Romania. She holds a PhD cum laude in applied electronics and telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers in refereed journals and for international conferences. Her research interests cover intelligent systems, fuzzy control, soft computing, smart sensors, information fusion, modeling, and simulation. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, as well as an expert evaluator for national and international projects and PhD theses. Dr. Balas is the Director of the Intelligent Systems Research Center and the Director of the Department of International Relations, Programs and Projects at the Aurel Vlaicu University of Arad. She served as the General Chair for nine editions of the International Workshop on Soft Computing Applications (SOFA) organized in 20052020 and held in Romania and Hungary. Dr. Balas participated in many international conferences as organizer, honorary chair, session chair, member in steering, advisory or international program committees, and keynote speaker. Now she is working on a national project funded by the European Union: BioCell-NanoART = Novel Bio-inspired Cellular Nano-Architectures. She is a member of the European Society for Fuzzy Logic and Technology, a member of the Society for Industrial and Applied Mathematics, a senior member of IEEE, a member of the IEEE Fuzzy Systems Technical Committee, the chair of Task Force 14 of the IEEE Emergent Technologies Technical Committee, a member of the IEEE Soft Computing Technical Committee. She is also the recipient of the "Tudor Tanasescu" prize from the Romanian Academy for contributions in the field of soft computing methods (2019).
