Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

Erscheinungsjahr 2019
ISBN: 978-0-12-818004-4
Verlag: Elsevier Science Publishing Co Inc

Buch, Englisch, 218 Seiten, Format (B × H): 190 mm x 234 mm, Gewicht: 450 g

Reihe: Advances in ubiquitous sensing applications for healthcare

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.

Autoren/Hrsg.

Dey, Nilanjan

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie

Inhaltsverzeichnis

1. Classification of Unhealthy and Healthy Neonates in Neonatal Intensive Care Units Using Medical Thermography Processing and Artificial 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

Über Autor(innen)

Dey, Nilanjan
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.