Applied Artificial Intelligence : A Biomedical Perspective book cover
1st Edition

Applied Artificial Intelligence
A Biomedical Perspective



  • Available for pre-order on June 20, 2023. Item will ship after July 11, 2023
ISBN 9781032349145
July 11, 2023 Forthcoming by CRC Press
384 Pages 166 Color & 54 B/W Illustrations

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USD $130.00

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Book 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.

Table of Contents

1. Healthcare Fees-centric to Value-centric Transformation through Data, Analytics and Artificial Intelligence. 
Sanjeev Manchanda, Mahesh Kshirsagar.
2. AI-based Healthcare: Top Businesses and Technologies. 
Dipali Ghatge, Dr. K. Rajeswari.
3. Insights into AI, Machine Learning and Deep Learning. 
Aditya Shinde, Swati Shinde.
4. Deep learning for visual perceptual brain decoding as Image Classification. 
Priyanka Jain, Saumya Kushwaha, N. K. Jain.
5. Automatic brain tumor segmentation in multimodal MRI images using deep learning. 
Seyyed-Mahdi Banan-Khojasteh, Mohammad-Ali Balafar.
6. Automated Prediction of Lung Cancer using Deep Learning Algorithms. 
S. Das, P. Kumar, S. Pal, S. Majumder.
7. CERVICAL CANCER SCREENING APPROACH USING AI. 
Dr.D.Santhi, Dr.M. Carmel Sobia, Dr. Jayalakshmi.
8. Progression Detection of Multiple sclerosis in Brain MRI Images. 
Santosh Chede. Smith, Surekha  Washimkar.
9. ARTIFICIAL INTELLIGENCE CLUSTERING TECHNIQUES ON DERMOSCOPIC SKIN LESION IMAGES. 
V.SaravanaKumar, S.AnanthaSivaprakasam, Sunil Bhutada, Suma K G, Lakshmi Priya, M. Kavitha.
10. Automated Alzheimer’s Disease Detection With Optimized Fuzzy Neural Network. 
Preeti Topannavar, Dr. D. M. Yadav, Dr. Varsha Bendre.
11. A comprehensive survey with bibliometric analysis on recent research opportunities of multimodal Medical Image Fusion in various applications. 
Manjiri A Ranjanikar, Nilam Upasani, Asmita Manna, Jaishri M. Waghmare, Shimpy Goyal, Rachana Y. Patil, Bharati P. Vasgi.
12. Big data in IoT for healthcare application. 
Nilam Upasani, Deepali Joshi, Sanika Upasani, Swayam Pendgaonkar.
13. Automatic Detection of Diabetic Retinopathy to avoid blindness. 
Smita Das, Sushanta Das, Saptarshi Debray, Madhusudhan Mishra, Swanirbhar Majumder.
14. A Review on Wireless BAN to measure the Respiration Rate using SoC Architecture. 
Archana H R, Surendra H.H, Jyothi A P, Lalitha S, Madhusudhan K N.
15. Deep Feature Extraction for EEG Signal Classification in Motor Imagery Tasks. 
Rashmi S, Vani Ashok.
16. Effect of Age in Normal Women by Heart Rate Variability Analysis. 
Anjali C.Birajdar, Vijaya R. Thool.
17. EEG signal analysis using machine learning and artificial intelligence for identification of brain dysfunction. 
Rajeswari Aghoram, Athira S. B.
18. Cervical Cancer Screening Methods: Comprehensive Survey. 
Swati Shinde, Madhura Kalbhor, Aditya Ankana, Aditya Shinde.
19. Understanding Assessment Methods and Sensors for ADHD Hyperactive-Impulsive Type Among Children. 
T. Kumar, M.B. Malarvili.
20. Security of Medical Image Information by Cryptography and Watermarking using Python. 
Pallavi R. Waghmare, Jaishri. M. Waghmare.
21. Integration of Biosensors and Drug Delivery Systems for Biomedical Applications. 
Jithu Jerin James, Sandhya KV.
22. Automatic Liver and Lesion Segmentation in CT Using 3D context Convolutional Neural network: 3D context U-Net. 
Lida Daryani Ghazani, MA. Balafar.
   
   
   
   
   
   
   

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Editor(s)

Biography

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 Bachelor’s 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.