Umfang:
1 Online-Ressource (XVII, 247 Seiten)
Ausgabe:
[Online-Ausgabe]
ISBN:
9783110708127
,
9783110708172
Serie:
Intelligent Biomedical Data Analysis 7
Inhalt:
Frontmatter -- Preface -- Acknowledgments -- Contents -- Short Biography of Editors -- List of Contributors -- Deep learning for health and medicine -- Exploring Indian Yajna and mantra sciences for personalized health: pandemic threats and possible cures in twenty-first-century healthcare -- Advanced deep learning techniques and applications in healthcare services -- Visualizations of human bioelectricity with internal symptom captures: the Indo-Vedic concepts on Healthcare 4.0 -- Early cancer predictions using ensembles of machine learning and deep learning -- Deep learning in patient management and clinical decision making -- Patient health record system -- Prediction of multiclass cervical cancer using deep machine learning algorithms in healthcare services -- Comparative analysis for detecting skin cancer using SGD-based optimizer on a CNN versus DCNN architecture and ResNet-50 versus AlexNet on Adam optimizer -- Coronary heart disease analysis using two deep learning algorithms, CNN and RNN, and their sensitivity analyses -- An overview of the technological performance of deep learning in modern medicine -- Index
Inhalt:
This book uncovers the stakes and possibilities involved in realising personalised healthcare services through efficient and effective deep learning algorithms, enabling the healthcare industry to develop meaningful and cost-effective services. This requires effective understanding, application and amalgamation of deep learning with several other computing technologies, such as machine learning, data mining, and natural language processing.
Anmerkung:
Mode of access: Internet via World Wide Web.
,
In English
Weitere Ausg.:
9783110708004
Weitere Ausg.:
Erscheint auch als Druckausgabe Deep learning for personalised healthcare services Berlin : De Gruyter, 2021 9783110708004
Weitere Ausg.:
3110708000
Sprache:
Englisch
Fachgebiete:
Informatik
Schlagwort(e):
Gesundheitswesen
;
Deep learning
;
Krebsforschung
;
Deep learning
;
Aufsatzsammlung
DOI:
10.1515/9783110708127
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