Artificial Intelligence Applications for Health Care
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This book takes an interdisciplinary approach by covering topics on health care and artificial intelligence. Data sets related to biomedical signals (ECG, EEG, EMG) and images (X-rays, MRI, CT) are explored, analyzed, and processed through different computation intelligence methods. Applications of computational intelligence techniques like artificial and deep neural networks, swarm optimization, expert systems, decision support systems, clustering, and classification techniques on medial datasets are explained. Survey of medical signals, medial images, and computation intelligence methods are also provided in this book.
- Covers computational Intelligence techniques like artificial neural networks, deep neural networks, and optimization algorithms for Healthcare systems
- Provides easy understanding for concepts like signal and image filtering techniques
- Includes discussion over data preprocessing and classification problems
- Details studies with medical signal (ECG, EEG, EMG) and image (X-ray, FMRI, CT) datasets
- Describes evolution parameters such as accuracy, precision, and recall etc.
This book is aimed at researchers and graduate students in medical signal and image processing, machine and deep learning, and healthcare technologies.
Table of Contents
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.
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.