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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis
1st Edition - July 31, 2019
Author: Nilanjan Dey
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 1 8 0 0 4 - 4
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 1 8 0 0 5 - 1
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… Read more
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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.
- Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges
- Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications
- Introduces several techniques for medical image processing and analysis for CAD systems design
Biomedical engineers and researchers in data analytics, soft computing, deep learning, and computer-aided diagnosis systems
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
- No. of pages: 218
- Language: English
- Published: July 31, 2019
- Imprint: Academic Press
- Paperback ISBN: 9780128180044
- eBook ISBN: 9780128180051
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Nilanjan Dey
Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, JIS University, Kolkata, India. He is a visiting fellow of the University of Reading, UK, and also holds the position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held the honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He got his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (Springer Nature), Data-Intensive Research (Springer Nature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He is an associate editor of IET Image Processing (Wiley) and an editorial board member of Complex & Intelligent Systems (Springer Nature), Applied Soft Computing (Elsevier), and more. He has written 110 books and over 300 other publications in the areas of medical imaging, machine learning, computer aided diagnosis, data mining, etc. His works have been cited over 15,000 times. He is India’s Ambassador to the International Federation for Information Processing—Young ICT Group and a senior member of IEEE.
Affiliations and expertise
Department of Computer Science & Engineering, Maulana Abul Kalam Azad JIS University, Agarpara, Kolkata, India.