Handbook of Deep Learning Models for Healthcare Data Processing

In recent years, deep learning has shown great potential in transforming various fields including healthcare. With the abundance of healthcare data being generated every day, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes. Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications covers a wide range of deep learning models, techniques, and applications in healthcare data processing, analysis, and disease prediction, providing a comprehensive overview of the field. It focuses on the practical application of deep learning models in healthcare and offers step-by-step instructions for building and deploying models and using real-world examples. The handbook discusses the potential future applications of deep learning models in healthcare, such as precision medicine, personalized treatment, and clinical decision support. It also addresses the ethical considerations associated with the use of deep learning models in healthcare, such as privacy, security, and bias. It provides technical details on deep learning models, including their architecture, training methods, and optimization techniques, making it useful for data scientists and researchers. Written to be a comprehensive guide for healthcare professionals, researchers, and data analysts, this handbook is an essential need for those who are interested in using deep learning models to analyze and process healthcare data. It is also suitable for those who have a basic understanding of machine learning and want to learn more about the latest advancements in deep learning in healthcare.

Prof. (Dr.) Ajay Kumar is currently serving as Professor (Research Track), Department of Mechanical Engineering, School of Core Engineering, Faculty of Science, Technology & Architecture, Manipal University Jaipur, Rajasthan, India. He received his Ph.D. in advanced manufacturing and automation from Guru Jambheshwar University of Science & Technology, Hisar, India after earning a B.Tech. with honors in mechanical engineering and an M.Tech. with distinction in manufacturing and automation. His areas of research include biomedical engineering, incremental sheet forming, artificial intelligence, sustainable materials, robotics and automation, additive manufacturing, mechatronics, smart manufacturing, industry 4.0, industrial engineering systems, waste management, and optimization techniques. He has over 100 publications in international journals of repute including SCOPUS and Web of Science as well as SCI-indexed database and refereed international conferences. He has organized various national and international events including an international conference on mechatronics and artificial intelligence (ICMAI-2021) as conference chair. He had organized an international conference on artificial intelligence, advanced materials, and mechatronics systems (AIAMMS-2023) as conference chair. He has more than 20 national and international patents to his credit. He has supervised more than eight M.Tech and Ph.D. scholars and numerous undergraduate projects/theses; he has a total of 15 years of experience in teaching and research. He has been a guest editor of many reputed journals and has contributed to many international conferences/symposiums as a session chair, expert speaker, and member of the editorial board. He has won several proficiency awards during the course of his career, including merit awards, best teacher awards, and so on. He has also coauthored or coedited more than 15 books and proceedings. Dr. Deepak Dembla has been the Dean and Head of Department of the School of Computer Applications at JECRC University in Jaipur for the last nine years; he is also the director of internships and of accreditation. He earned his in M.Tech. in IT from Punjabi University Patiala in 2004 and his MCA and Ph.D. from Guru Jambheshwar University. He earned his postgraduate degree in business management in 1996, has total experience of 22 years, and specializes in mobile ad hoc networks, wireless networks, software engineering, cloud computing, AI, & machine learning. He has published 61 research papers in international and national journals and including prestigious international SCI-listed journals and at conferences such as IEEE (Institute of Electrical and Electronics Engineers) and ACM (Association for Computing Machinery). He is on the editorial board of various international journals, has guided a dozen M.Tech. students, and is actively guiding 10 research scholars for their Ph.D. programs. He is associated with various professional international societies (ACM, IEEE, International Association of Engineers, International Association of Computer Science and Information Technology, etc.) and has 10 patents including one in Germany. Dr. Seema Tinker is a Professor in the Mathematics Department of JECRC University, who has over 22 years of cumulative academic experience. She obtained her Ph.D. in mathematics in 2006 from the University of Rajasthan, Jaipur, India. Her research emphasizes multidisciplinary topics encompassing blockchain, machine learning, deep learning, fluid dynamics, and relativity. She has published over 35 articles in esteemed international publications, including those indexed in Scopus, Web of Science, and SCI, and has presented more than 20 papers at national and international conferences. She examined several journal articles from Web of Science and Scopus. Dr. Tinker has taken part in over 23 workshops, faculty development programs, and short-term courses, including IIT Bombay Foundation Programs in ICT for education, as well as ATM Workshops, and has earned ten certifications in machine and deep learning from leading institutions, including Stanford University and Imperial College London. She has produced three books and has five patents on machine and deep learning topics, one of which has been issued in Germany. Her commitment and ability have been acknowledged via several prizes, including the esteemed "Best Faculty Award" and "Certificate of Excellence." Additionally, she has been appointed as an instructor in reputed academic programs alongside professors from IIT Delhi, IIT Roorkee, and IIT Bombay in the Online Instructional School for Teachers organized by the National Centre for Mathematics. She has also fulfilled roles as session chair, co-chair, member of the organizing committees, reviewer, and keynote speaker at various respected conferences. Surbhi Bhatia Khan holds a doctorate of computer science and engineering in machine learning and social media analytics. She earned professional certification from the reputed Project Management Institute, United States. She is currently a lecturer in the Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester, United Kingdom. She has more than 11 years of academic and teaching experience at different universities and has published 100+ papers in many reputed journals in high indexed outlets as well as authoring or editing 14 books. She has around 12 international patents from India, Australia, and the United States. Dr. Khan has completed research projects funded by the Deanship of Scientific Research, Ministry of Education from Saudi Arabia and India. She recently submitted a UK Research and Innovation project as a co-project investigator with a grant of GBP 400,000 from the Arts and Humanities Research Council. She is also leading a project with Majmah University in Saudi Arabia under a grant of SAR 1.5 million from the King Salman Centre for Disability Research. Dr. Khan is a senior member of IEEE and a member of IEEE Young Professionals and ACM. She has chaired several international conferences and workshops and has delivered over 20 invited and keynote talks across the globe. She also enjoys a position as adjunct professor at Shoolini University, Himachal Pradesh, India. She is also an academic editor, associate editor, or guest editor for many reputed journals and she received the Research Excellence award from King Faisal University in Saudi Arabia in 2021. Her areas of interest are information systems, sentiment analysis, machine learning, databases, and data science.

Section 1: Emerging Technologies of Deep Learning in Healthcare. 1. Deep Learning Models for Electronic Health Record (EHR) Data Analysis. 2. An Extensive Study of Disease Prediction Models using Machine Learning. 3. Deep Learning Approaches for Alzheimer's disease Diagnosis: A Comparative Study of ResNet50, CNN, and MobileNet. 4. Sentiment Classification Analysis Using Deep Learning Network Models. 5. Predictive Modeling of Herbal-Drug Interactions using Mathematical Approaches. 6. Revolutionizing Breast Cancer Detection: A Shallow Neural Network Approach for Accurate Classification of Calcifications and Masses in Mammographic Scans. 7. Artificial Intelligence-Based Automated Detection of Rheumatoid Arthritis: A Review. 8. Medical Imaging Analysis Techniques: Advances, Challenges, and Future Directions. 9. Modeling the Transtheoretical Model for Health Behavior Stage Analysis: Tool Development and Testing. Section 2: Deep Learning Analytics in Healthcare. 10. Utilization of OCR and LLM to decode medical diagnostics/prescriptions into general-purpose language. 11. A state-of-the-art model for drug classification using image recognition. 12. Transforming Healthcare with Blockchain-based Smart Contracts: A Focus on Quality-of-Service. 13. Prototype Model for Face and Skin-Related Disease Detection Using Deep Learning and Image Recognition. 14. Brain Computer Interface (BCI)-Inspired Arduino Based Robotic Brain Controller. 15. Transfer Learning-based Framework for Human Skin Cancer Evaluation . 16. Healthcare Reimagined: AI's Impact on Diagnosis and Treatment. 17. Advanced LSTM Approach for Aspect-based Sentiment Classification. 18. A Review on Patch-based Medical Image Classification using Convolutional Neural Network (CNN).