Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis

Herausgeber: Sharma, Smita; Bashir, Ali Kashif; Ramesh, S.; Balusamy, Balamurugan

• Broschiertes Buch

Produktbeschreibung

Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analysis. The book delves into various use cases where deep learning is applied in industrial, social, and personal contexts within the biomedical domain. By gaining a comprehensive understanding of deep learning in biomedical data analysis, readers will develop the skills to critically evaluate research papers, methodologies, and emerging trends. In 14 chapters this book provides both insights into the fundamentals as the latest research trends in the applications of deep learning in biosciences. With several case studies and use cases it familiarizes the reader with a comprehensive understanding of deep learning algorithms, architectures, and methodologies specifically applicable to biomedical data analysis. This title is an ideal reference for researchers across the biomedical sciences.

Produktdetails

• Verlag: Elsevier Health Sciences
• Seitenzahl: 330
• Erscheinungstermin: 1. März 2025
• Englisch
• ISBN-13: 9780443267659
• ISBN-10: 0443267650
• Artikelnr.: 71302887

Inhaltsangabe

1. A detailed perspective of biomedical engineering
2. Latest research trends and Transitions in the Domain of biomedical
engineering
3. The brewing challenges and concerns of biomedical engineering
4. The significance of computer vision for biomedical engineering
5. Fundamentals and Technology Roadmap of Deep Learning
6. Deep Learning Techniques for Biomedical Image Analysis
7. Convergence of Deep Learning, AI for Genomic Data Analysis
8. Deep Learning in Electronic Health Records (EHR) Analysis
9. Deep Learning for Wearable Sensor Data Analysis
10. Deep Learning for Drug Discovery and Development
11. Deep Learning for Pharmacokinetics and Pharmacodynamics
12. Ethical Considerations and Challenges in Deep Learning for Biomedical
Data Analysis
13. The Societal Impact and Emerging Trends
14. Case Studies and Use Cases of Deep Learning for biomedical applications
15. The Role of Generative AI for biomedical engineering