With multidisciplinary mechanisms in the biomedical field, physicians can successfully deal with critical health care problems that are recognized through medical imaging technologies like X-Ray, CT(Computed Tomography), MRI(Magnetic Resonance Imaging), PET(Positron Emission Tomography), advance prediction of abnormality through big data analytics, use of Internet of Things and semantic technology for the health care monitoring and many others.
Broadly “Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems” consists of various phases of medical imaging and medical data analytics that include image acquisition, image enhancement, and complicated hidden feature extraction, Image segmentation, post-processing of the image for abnormality identification and incorporation of evolutionary computation, fuzzy logic, Natural Computation, Artificial Neural Network, Classification techniques for the better analyzing and formulating outcome.
The book “Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems” incorporates remarkable and outstanding contributions in computer-aided diagnosis and biomedical analytics that can benefit diverse areas of biomedical engineering, taking into account biomedical imaging, Computational Medicine, Diagnosis, Health informatics, implants, and Medical Devices, and Healthy Systems Engineering. It provides a comprehensive study of various machine learning algorithms and deep neural network based approaches in biomedical engineering. It would be focusing on models, methods, and applications in Image Analysis, Multimodal Imaging Mechanisms, Assistive Technology, Telemedicine, and Interdisciplinary approaches for the future horizon. It serves as an absolute information provider of the multidisciplinary mechanisms used in Biomedical Engineering and their outcomes.
Editors:
- P. Naga Srinivasu;
- Norita Md Norwawi;
- Sheng Lung Peng;
- Azuraliza Abu Bakar
Keywords:
Soft Computing, Machine Learning, Deep Learning, Neural Networks, Evolutionary algorithms, Medical Imaging, Smart Diagnosis, Illness Prediction
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