Deep Learning for Medical Applications with Unique Data

Deep Learning for Medical Applications with Unique Data

1st Edition - February 15, 2022

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  • Editors: Deepak Gupta, Utku Kose, Ashish Khanna, Valentina Emilia Balas
  • eBook ISBN: 9780128241462
  • Paperback ISBN: 9780128241455

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Description

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.

Key Features

  • 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

Readership

Biomedical engineers and researchers in biomedical engineering, applied informatics, Artificial Intelligence, and data science. Computer scientists, as well as students in electronics, communication engineering, and information technology.

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • About the editors
  • Foreword
  • Preface
  • 1. A deep learning approach for the prediction of heart attacks based on data analysis
  • 1. Introduction
  • 2. Literature survey
  • 3. Materials and method
  • 4. Training models
  • 5. Data preparation
  • 6. Results
  • 7. Conclusion
  • 8. Note
  • 2. A comparative study on fully convolutional networks—FCN-8, FCN-16, and FCN-32: A case of brain tumor
  • 1. Introduction
  • 2. Literature study
  • 3. Discussion and results
  • 4. Conclusion
  • 3. Deep learning applications for disease diagnosis
  • 1. Introduction
  • 2. Deep learning
  • 3. Methods of evaluation
  • 4. Unique data
  • 5. Current situation of deep learning in disease diagnosis
  • 6. Advantages of deep learning in medical diagnosis
  • 7. Applications
  • 8. Shortcomings
  • 9. Conclusion and future scope
  • 4. An artificial intelligent cognitive approach for classification and recognition of white blood cells employing deep learning for medical applications
  • 1. Introduction
  • 2. Cognitive computing concept
  • 3. Neural networks concepts
  • 4. Metaheuristic algorithm proposal
  • 5. Results and discussion
  • 6. Future research directions
  • 5. Deep learning on medical image analysis on COVID-19 x-ray dataset using an X-Net architecture
  • 1. Introduction
  • 2. Literature review
  • 3. Data set and image augmentation
  • 4. Convolutional neural network architectures and proposed model
  • 5. Results and discussion
  • 6. Detecting x-ray images through prediction
  • 7. Conclusion and future scope
  • 6. Early prediction of heart disease using deep learning approach
  • 1. Introduction
  • 2. Related study
  • 3. Dataset
  • 4. Classification techniques and performance analysis
  • 5. Conclusion
  • 6. Discussion
  • 7. Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system
  • 1. Introduction
  • 2. Machine learning: Regression models
  • 3. Machine learning algorithms
  • 4. Deep learning models
  • 5. Conclusion
  • Appendix 1. Models of FCN-8, FCN-16, and FCN-32
  • 8. Automatic detection of white matter hyperintensities via mask region-based convolutional neural networks using magnetic resonance images
  • 1. Introduction
  • 2. Related works
  • 3. Material and methods
  • 4. Experimental results
  • 5. Discussion and conclusion
  • 9. Diagnosing glaucoma with optic disk segmenting and deep learning from color retinal fundus images
  • 1. Introduction
  • 2. Related work
  • 3. Methodology
  • 4. Results and discussion
  • 5. Conclusion
  • 10. An artificial intelligence framework to ensure a trade-off between sanitary and economic perspectives during the COVID-19 pandemic
  • 1. Introduction to artificial intelligence methods employed to tackle the COVID-19 pandemic
  • 2. State of the art
  • 3. General description of the trade-off model
  • 4. Methods adapted to the field of COVID-19–related applications
  • 5. Impacts of sanitary measures on the economy
  • 6. Conclusion
  • 11. Prediction of COVID-19 using machine learning techniques
  • 1. Introduction
  • 2. Motivation
  • 3. Applications of AI, machine learning, and deep learning
  • 4. Coronavirus disease-2019 prediction using machine learning
  • 5. Conclusion
  • Index

Product details

  • No. of pages: 256
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: February 15, 2022
  • Imprint: Academic Press
  • eBook ISBN: 9780128241462
  • Paperback ISBN: 9780128241455

About the Editors

Deepak Gupta

Dr. Deepak Gupta received a B.Tech. degree in 2006 from the Guru Gobind Singh Indraprastha University, Delhi, India. He received an M.E. degree in 2010 from Delhi Technological University, India, and a PhD in 2017 from Dr. APJ Abdul Kalam Technical University, Lucknow, India. He completed a post-doc degree at the National Institute of Telecommunications (Inatel), Brazil, in 2018. He has co-authored more than 155 journal articles, including 110 SCI papers and 45 conference articles. He has authored or edited 54 books published by IEEE-Wiley, Elsevier, Springer, Wiley, CRC Press, De Gruyter, and Katsons. He holds four patents in India. He is a convener of ICICC, ICDAM, and the DoSCI Springer conferences series. Currently, he is an associate editor of 'Expert Systems' (Wiley) and 'Intelligent Decision Technologies' (IOS Press). He is the recipient of the 2021 IEEE System Council Best Paper Award. He has been featured in the list of top 2% scientist/researcher in the world. He is working toward promoting start-ups and also serving as a consultant. Moreover, he is a series editor of 'Elsevier Biomedical Engineering' (Academic Press), 'Intelligent Biomedical Data Analysis' (De Gruyter), and 'Explainable AI (XAI) for Engineering Applications' (CRC Press).

Affiliations and Expertise

Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology (MAIT), New Delhi, India

Utku Kose

Dr. Utku Kose is an Associate Professor at Su¨leyman 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 articles, authored and edited books, proceedings, and reports. He is also a series editor of the 'Biomedical and Robotics Healthcare' (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.

Affiliations and Expertise

Associate Professor, Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

Ashish Khanna

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.

Affiliations and Expertise

Sr. Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology (MAIT), New Delhi, India

Valentina Emilia Balas

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 2005–2020 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).

Affiliations and Expertise

Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, Romania

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