Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images.
Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images.
1. Classıfıcatıon of Unhealthy and Healthy Neonates in Neonatal Intensıve Care Unıts Usıng Medıcal Thermography Processıng and Artıfıcıal Neural Network2. Use of Health-related Indices and Cassification Methods in Medical Data3. Image Analysis for Diagnosis and Early Detection of Hepatoprotective Activity4. Characterization of Stuttering Dysfluencies using Distinctive Prosodic and Source Features5. A Deep Learning Approach for Patch-based Disease Diagnosis from Microscopic Images6. A Breast Tissue Characterization Framework Using PCA and Weighted Score Fusion of Neural Network Classifiers7. Automated Arrhythmia Classification for Monitoring Cardiac Patients Using Machine Learning Techniques8. IoT-based Fluid and Heartbeat Monitoring For Advanced Healthcare
Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in
information technology from West Bengal Board of Technical
University and Ph.D. degrees in electronics and telecommunication
engineering from Jadavpur University, Kolkata, India, in 2005,
2011, and 2015, respectively. Currently, he is Associate Professor
with the Techno International New Town, Kolkata and a visiting
fellow of the University of Reading, UK. He has authored over 300
research articles in peer-reviewed journals and international
conferences and 40 authored books. His research interests include
medical imaging and machine learning. Moreover, he actively
participates in program and organizing committees for prestigious
international conferences, including World Conference on Smart
Trends in Systems Security and Sustainability (WorldS4),
International Congress on Information and Communication Technology
(ICICT), International Conference on Information and Communications
Technology for Sustainable Development (ICT4SD) etc.
He is also the Editor-in-Chief of International Journal of Ambient
Computing and Intelligence, Associate Editor of IEEE Transactions
on Technology and Society and series Co-Editor of Springer Tracts
in Nature-Inspired Computing and Data-Intensive Research from
Springer Nature and Advances in Ubiquitous Sensing Applications for
Healthcare from Elsevier etc. Furthermore, he was an Editorial
Board Member Complex & Intelligence Systems, Springer, Applied Soft
Computing, Elsevier and he is an International Journal of
Information Technology, Springer, International Journal of
Information and Decision Sciences etc. He is a Fellow of IETE and
member of IE, ISOC etc.
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