Handbook of Deep Learning Models for Healthcare Data Processing

Disease Prediction, Analysis, and Applications
1. Auflage 2025
ISBN: 978-1-032-73939-7
Verlag: Taylor & Francis Ltd

Buch, Englisch, 304 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Advancements in Intelligent and Sustainable Technologies and Systems

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 Health 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.

Zielgruppe

Professional Reference

Autoren/Hrsg.

Kumar, Ajay

Dembla, Deepak

Tinker, Seema

Bhatia Khan, Surbhi

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik Mathematik Operations Research

Inhaltsverzeichnis

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).

Über Autor(innen)

Prof. (Dr.) Ajay Kumar is currently serving as a Professor in School of Engineering and Technology, JECRC University, Jaipur, Rajasthan, India. He received his Ph.D. in the field of Advanced Manufacturing from Guru Jambheshwar University of Science & Technology, Hisar, India after B.Tech. (Hons.) in mechanical engineering and M.Tech. (Distinction) in manufacturing and automation. Dr. Kumar’s areas of research include Incremental Sheet Forming, Artificial Intelligence, Sustainable Materials, Additive Manufacturing, Mechatronics, Smart Manufacturing, Industry 4.0, Waste Management, and Optimization Techniques. He has authored over 120 publications in international journals of repute including SCOPUS, Web of Science and SCI indexed database and refereed international conferences. As well as co-authored and co-edited many books with ELSEVIER, CRC Press, WILEY, De Gruyter and IGI Global and conference proceedings with IOP. In addition to organizing various national and international events including an international conference on Mechatronics and Artificial Intelligence (ICMAI-2021), Dr. Kumar was the conference chair of the International conference on Artificial Intelligence, Advanced Materials, and Mechatronics Systems (AIAMMS-2023). He has more than 20 national and international patents to his credit, and has supervised more than eight M.Tech, Ph.D scholars and numerous undergraduate projects/thesis during his 15 years of teaching and research experience. Dr. Kumar is a Guest Editor and Review Editor of reputed journals including Frontiers in Sustainability, and has contributed to many international conferences/symposiums as a session chair, expert speaker, and member of the editorial board. During his career Prof Kumar has won several proficiency awards, including merit awards, and best teacher awards. He is adviser of QCFI, Delhi Chapter student cell at JECRC University and has authored many in-house course notes, lab manuals, monographs and invited chapters in books. Additionally, he organized a series of Faculty Development Programs, International Conferences, workshops, and seminars for researchers, PhD, UG and PG level students and is associated with multiple research, academic, and professional societies.

Dr. Deepak Dembla has been serving JU as Dean and HoD of the School of Computer Applications in JECRC University Jaipur for the last 9 years. He is also the Director of Internships and Accreditation. Dr. Dembla completed his M. Tech. from Punjabi University Patiala in the year 2004 and his Ph.D. from Guru Jambheshwar University of Science and Technology Hisar, NAAC Accredited A grade State Govt. University in Haryana. He has 22 years of experience and specialization in Mobile Adhoc Networks, Wireless Networks, Software Engineering, Cloud Computing, AI& ML. Dr. Dembla has published 61 research papers in international and national journals and conferences of repute. He is on the editorial board of various international journals, has guided a dozen students of M. Tech., and is actively guiding 10 research scholars for their Ph.D. programs. He is also associated with various professional international societies like ACM, IEEEE, IAENG, IACSIT and has published 10 Patents including the grant of one German Patent as well.

Dr. Seema Tinker is working as a Professor in the Department of Mathematics and has a total academic experience of more than 20 years. She was awarded a Ph.D. in Mathematics in the year 2006 from Rajasthan University Jaipur, India. Her research work includes Relativity, Fluid Dynamics, and Machine and Deep Learning. She has published more than 22 research papers in national and international SCOPUS/SCI journals and has also published more than 20 papers in various national and international conferences. Dr. Tinker has attended more than 23 workshops, FDPs, and short-term courses and has authored 3 books and published 3 Indian Patents.

Dr. Surbhi Bhatia Khan has a doctorate in Computer Science and Engineering in Machine Learning and Social Media Analytics. She earned a Project Management Professional Certification from the Project Management Institute, USA. Dr. Khan is currently working as a Lecturer in the Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester, United Kingdom, and has more than 11 years of academic and teaching experience at different universities. Dr. Khan has published 100+ papers in many reputed journals in high-indexed outlets and has around 12 international patents from India, Australia, and the USA. She has successfully authored and edited 14 books and has completed research-funded projects from the Deanship of Scientific Research, and the Ministry of Education in Saudi Arabia, and India. She is a senior member of IEEE, 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. Her areas of interest are Information Systems, Sentiment Analysis, Machine Learning, Databases, and Data Science.