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.
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.
1. A deep learning approach for the prediction of heart attacks
based on data analysis
2. A comparative study on fully convolutional networks—FCN-8,
FCN-16, and FCN-32: A case of brain tumor
3. Deep learning applications for disease diagnosis
4. An artificial intelligent cognitive approach for classification
and recognition of white blood cells employing deep learning for
medical applications
5. Deep learning on medical image analysis on COVID-19 x-ray
dataset using an X-Net architecture
6. Early prediction of heart disease using a deep learning
approach
7. Machine learning and deep learning algorithms in disease
prediction: Future trends for the healthcare system
8. Automatic detection of white matter hyperintensities via mask
region-based convolutional neural networks using magnetic resonance
images
9. Diagnosing glaucoma with optic disk segmenting and deep learning
from color retinal fundus images
10. An artificial intelligence framework to ensure a trade-off
between sanitary and economic perspectives during the COVID-19
pandemic
11. Prediction of COVID-19 using machine learning techniques
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 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).
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