Deep Learning in Biomedical and Health Informatics (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
202
Utgivningsdatum
2021-09-27
Förlag
CRC Press
Medarbetare
Abraham, Ajith / Dogan, Onur
Illustratör/Fotograf
black and white 24 Tables 26 Line drawings, black and white 35 Halftones black and white 61
Illustrationer
24 Tables, black and white; 26 Line drawings, black and white; 35 Halftones, black and white; 61 Ill
Dimensioner
238 x 154 x 20 mm
Vikt
499 g
Antal komponenter
1
Komponenter
52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam
ISBN
9780367726041
Deep Learning in Biomedical and Health Informatics (inbunden)

Deep Learning in Biomedical and Health Informatics

Current Applications and Possibilities

Inbunden Engelska, 2021-09-27
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Provides a proficient guide on the relationship between AI and healthcare and how AI is changing all aspects of the health care industry Covers how deep learning will help in the diagnosis and the prediction of disease spread Presents a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing Highlights various challenges in applying the deep learning in health care Promotes research in deep learning application in understanding the biomedical process
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Övrig information

Dr. M. A. JABBAR is a Professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Dr Ajith Abraham is the Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing and a Distinguished Lecturer of IEEE Computer Society representing Europe (2011-2013). Dr. Onur Dogan is an assistant professor at Izmir Bakircay University. Dr Ana Madureira has a PhD degree in Production and Systems from University of Minho, Portugal. Dr. Sanju Tiwari is a Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico.

Innehållsförteckning

1. Foundations of Deep Learning and its Applications to Health Informatics. 2. Deep Knowledge Mining of Complete HIV Genome Sequences in Selected African Cohorts. 3. Review of Machine Learning Approach for Drug development Process. 4. A Detailed Comparison of Deep Neural Networks for Diagnosis of COVID-19. 5. Deep Learning in BioMedical Applications: Detection of Lung Disease with Convolutional Neural Networks 6. Deep Learning Methods For Diagnosis Of Covid-19 using Radiology Images And Genome Sequences: Challenges And Limitations. 7. Applications of Lifetime Modeling with Competing Risks in Biomedical Sciences. 8. PeNLP Parser: An Extraction and Visualization Tool for Precise Maternal, Neonatal and Child Healthcare Geo-locations from Unstructured Data. 9. Recent Trends in Deep learning, Challenges and Opportunities