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.
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.
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 D. Santhi, M. Carmel Sobia, and M. Jayalakshmi 8 Progression
Detection of Multiple Sclerosis in Brain MRI Images 9 Artificial Intelligence
Clustering Techniques on Dermoscopic Skin Lesion Images 10 Automated
Alzheimers 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 3-D Context Convolutional Neural Network: 3-D
Context U-Net
Dr. Swati V. Shinde has a Ph.D. in Computer Science and Engineering, from Swami Ramanand Teerth Marathwada University, Nanded. She has 20 years of teaching experience and is currently working as a Professor at Pimpri Chinchwad College of Engineering (PCCoE), Pune. She has worked as a HOD-IT for seven years in PCCoE. Her research interests include Machine Learning, Deep Learning, Soft Computing, Artificial Neural Network, and Fuzzy Logic.
Dr. Varsha Bendre received a Bachelors degree in Electronics and Telecommunication Engineering from Saint Gadge Baba Amravati University, Amravati, and M.E degree from Savitribai Phule Pune University in 2000 and 2010 respectively. She completed a Ph.D. in the area of Nanotechnology and Low Power VLSI from Savitribai Phule Pune University, Pune, and Maharashtra, India in Jan 2020. Her research work is focused on analog circuit design at very deep submicron technology using Carbon Nanotube Field-Effect Transistors.
Dr. D. Jude Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006, and Ph.D. from Karunya University in 2013. His research areas include Computational Intelligence and Image processing.
Dr. MA Balafar completed his Ph.D. in IT from UPM, Malaysia. He has 16 years of teaching experience and is working as an Assistant Professor at the University of Tabriz, Iran. His research interests are AI, computer vision, Fuzzy Logic, Deep Learning, Machine Learning, and information security.
