Advances in Artificial Intelligence

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ISBN/EAN: 9780443153921
Artificial Intelligence in health care has become one of the best assisting techniques for clinicians in proper diagnosis and surgery. In biomedical applications, artificial intelligence algorithms are explored for bio-signals such as electrocardiogram (ECG/ EKG), electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG), blood pressure, heart rate, nerve conduction, etc., and for bio-imaging modalities, such as Computed Tomography (CT), Cone-Beam Computed Tomography (CBCT), MRI (Magnetic Resonance Imaging), etc. Advancements in Artificial intelligence and big data has increased the development of innovative medical devices in health care applications. Recent Advances in Artificial Intelligence: Medical Applications provides an overview of artificial intelligence in biomedical applications including both bio-signals and bio-imaging modalities. The chapters contain a mathematical formulation of algorithms and their applications in biomedical field including case studies. Biomedical engineers, advanced students, and researchers can use this book to apply their knowledge in artificial intelligence-based processes to biological signals, implement mathematical models and advanced algorithms, as well as develop AI-based medical devices. - Covers the recent advancements of artificial intelligence in healthcare, including case studies on how this technology can be used - Provides an understanding of the design of experiments to validate the developed algorithms - Presents an understanding of the versatile application of artificial intelligence in bio-signal and bio-image processing techniques
Autor: Kunal Pal, Bala Chakravarthy Neelapu, J. Sivaraman
EAN: 9780443153921
eBook Format: PDF/ePUB
Sprache: Englisch
Produktart: eBook
Veröffentlichungsdatum: 21.05.2024
Untertitel: Biomedical Engineering Applications in Signals and Imaging
Kategorie:
Schlagworte: Healthcare biomedical applications biomedical imaging biomedical signal deep learning machine learning medical devices reinforcement learning smart diagnosis

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