Mitul Kumar Ahirwal, Narendra D. Londhe, Anil Kumar

Artificial Intelligence Applications for Health Care (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book takes an interdisciplinary approach covering health care, cognitive computing, and artificial intelligence. Data sets related to biomedical signals (ECG, EEG, EMG) and images (X-rays, MRI, CT) are explored, analyzed, and processed.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 332
• Erscheinungstermin: 19. April 2022
• Englisch
• ISBN-13: 9781000570342
• Artikelnr.: 63755852

Autorenporträt

Mitul K. Ahirwal is currently working as an Assistant Professor in the Department of Computer Science and Engineering at, Maulana Azad National Institute of Technology Bhopal, India. His research area is biomedical signal processing, swarm optimization, Brain Computer Interface and Healthcare system. He has been involved as a reviewer with various reputed journals.

Narendra D. Londhe is currently working with National Institute of Technology Raipur as an Associate Professor in Department of Electrical Engineering. His area of research includes Image and signal processing, soft computing, Biometrics, Ultrasound Imaging, IVUS imaging, Brain computer interface, Pain assessment and Psoriasis severity detection.

Anil Kumar joined as an Assistant Professor in the Electronic & Communication Engineering Department, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, India since 2009 to July 2016. His academic and research interest is design of Digital Filters & Multirate Filter Bank, Multirate Signal Processing, Biomedical Signal Processing, Image Processing, and Speech Processing.

Inhaltsangabe

1. A Survey of Machine Learning in Healthcare. 2. A Review on Biomedical
Signals with Fundamentals of Digital Signal Processing. 3. Images in
Radiology : Concepts of Image Acquisition and the Nature of Images. 4.
Fundamentals of Artificial Intelligence and Computational Intelligence
Techniques with their Applications in Healthcare Systems. 5. Machine
Learning Approach with Data Normalization Technique for Early Stage
Detection of Hypothyroidism. 6. GPU-based Medical Image Segmentation: Brain
MRI Analysis Using 3D Slicer. 7. Preliminary Study of Retinal Lesions
Classification on Retinal Fundus Images for The Diagnosis of Retinal
Diseases. 8. Automatic Screening of COVID-19 based on CT Scan Images
through Extreme Gradient Boosting. 9. Investigations on Convolutional
Neural Network in Classification of the Chest X-Ray Images for COVID-19 and
Pneumonia. 10. Improving the Detection of Abdominal and Mediastinal Lymph
Nodes in CT Images Using Attention U-Net Based Deep Learning Model. 11.
Swarm Optimized Hybrid Layer Decomposition and Reconstruction Model for
Multi-Modal Neurological Image Fusion. 12. Hybrid Seeker Optimization
Algorithm-Based Accurate Image Clustering for Automatic Psoriasis Lesion
Detection. 13. A COVID-19 Tracker for Medical Front-Liners. 14.
Implementation of One Dimensional Convolutional Neural Network for ECG
Classification on Python. 15. Pneumonia Detection from X-ray Images by Two
Dimensional Convolutional Neural Network on Python Platform.