Main description:

This book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like AI, IoT, and Signal Processing. It will also contribute to biosensors, MEMS, and related research. Applied Artificial Intelligence: Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems. Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis which have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyber-physical systems that facilitates computing anywhere by using Medical IoT and biosensors and the numerous applications of this technology in the healthcare domain. Part IV describes the different signal processing applications in the healthcare domain. It also includes the prediction of some human diseases based on the inputs in signal format. Part V highlights the scope and applications of MEMS and biosensors in the biomedical field. This includes the topics on robotic surgeries, human-robot interaction, biomechanics and transplants, drug delivery, etc. Part VI covers the latest trends in the biomedical field including electronic health records (EHRs) which is an emerging area in the healthcare domain. The chapters also include a blend of many healthcare use cases using AI as a solution that can be readily deployed by the industry. The book will be beneficial to the researchers, industry persons, faculty, and students working in biomedical applications of Computer Science and Electronics Engineering. It will also be a useful resource for teaching courses like AI/ML, Medical IoT, Signal Processing, Biomedical Engineering, and Medical Image Analysis.

Contents:

1. Healthcare Fees-centric to Value-centric Transformation through Data, Analytics and Artificial Intelligence. 2. AI-based Healthcare: Top Businesses and Technologies. 3. Insights into AI, Machine Learning and Deep Learning. 4. Deep learning for visual perceptual brain decoding as Image Classification. 5. Automatic brain tumor segmentation in multimodal MRI images using deep learning. 6. Automated Prediction of Lung Cancer using Deep Learning Algorithms. 7. CERVICAL CANCER SCREENING APPROACH USING AI. 8. Progression Detection of Multiple sclerosis in Brain MRI Images. 9. ARTIFICIAL INTELLIGENCE CLUSTERING TECHNIQUES ON DERMOSCOPIC SKIN LESION IMAGES. 10. Automated Alzheimer's Disease Detection With Optimized Fuzzy Neural Network. 11. A comprehensive survey with bibliometric analysis on recent research opportunities of multimodal Medical Image Fusion in various applications. 12. Big data in IoT for healthcare application. 13. Automatic Detection of Diabetic Retinopathy to avoid blindness. 14. A Review on Wireless BAN to measure the Respiration Rate using SoC Architecture. 15. Deep Feature Extraction for EEG Signal Classification in Motor Imagery Tasks. 16. Effect of Age in Normal Women by Heart Rate Variability Analysis. 17. EEG signal analysis using machine learning and artificial intelligence for identification of brain dysfunction. 18. Cervical Cancer Screening Methods: Comprehensive Survey. 19. Understanding Assessment Methods and Sensors for ADHD Hyperactive-Impulsive Type Among Children. 20. Security of Medical Image Information by Cryptography and Watermarking using Python. 21. Integration of Biosensors and Drug Delivery Systems for Biomedical Applications. 22. Automatic Liver and Lesion Segmentation in CT Using 3D context Convolutional Neural network: 3D context U-Net.