Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis
Editat de Smita Sharma, Balamurugan Balusamy, S. Ramesh, Ali Kashif Bashiren Limba Engleză Paperback – 22 apr 2025
- Provides a succinct overview of the cutting-edge technologies that are altering disease diagnosis, patient monitoring, and medical research
- Bridges the gap between biomedical engineering and deep learning by providing a comprehensive resource for comprehending the intersection of these disciplines
- Investigates how deep learning may change healthcare by providing new insights, diagnostics, and treatments via intelligent biomedical systems
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Specificații
ISBN-13: 9780443267659
ISBN-10: 0443267650
Pagini: 330
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE
ISBN-10: 0443267650
Pagini: 330
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE
Cuprins
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
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