book

Computational Analysis and Deep Learning for Medical Care

by Amit Kumar Tyagi

August 2021

Intermediate to advanced

528 pages

13h 4m

English

Wiley-Scrivener

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1.1 Introduction1.2 Various CNN Models1.3 Application of CNN to IVD Detection1.4 Comparison With State-of-the-Art Segmentation Approaches for Spine T2W Images1.5 ConclusionReferences
2.1 Introduction2.2 Related Work2.3 Artificial Intelligence Perspective2.4 Architecture2.5 ConclusionReferences
3.1 Introduction3.2 Related Works3.3 Convolutional Neural Networks3.4 Transfer Learning3.5 System Model3.6 Results and Discussions3.7 ConclusionReferences
4.1 Introduction4.2 Related Works4.3 Proposed Method4.4 Results and Discussion4.5 ConclusionReferences

5.1 Introduction5.2 Related Work5.3 Motivation5.4 Scope of the Work in Past, Present, and Future5.5 Deep Learning Tools, Methods Available for Clinical, and Health Informatics5.6 Deep Learning: Not-So-Near Future in Biomedical Imaging5.7 Challenges Faced Toward Deep Learning Using in Biomedical Imaging5.8 Open Research Issues and Future Research Directions in Biomedical Imaging (Healthcare Informatics)5.9 ConclusionReferences
6.1 Introduction6.2 Overview of Deep Learning Algorithms6.3 Other Deep Learning Architecture6.4 Biomedical Image Segmentation6.5 ConclusionReferences
7.1 Introduction7.2 Related Works7.3 Materials and Methods7.4 Experiments and Results7.5 ConclusionReferences
8.1 Introduction8.2 Problem Statement8.3 Conclusion8.4 SummaryReferences
9.1 Introduction9.2 Overview of System9.3 Methodology9.4 Performance and Analysis9.5 Experimental Results9.6 Conclusion and Future ScopeReferences
10.1 Introduction10.2 Machine Learning and Its Application10.3 Related Work10.4 Why Deep Learning on Top of Machine Learning?10.5 How is Deep Learning Used for Prediction of Lungs Cancer?10.6 ConclusionReferences
11.1 Introduction11.2 Background11.3 Methods11.4 Application of Deep CNN for Mammography11.5 System Model and Results11.6 Research Challenges and Discussion on Future Directions11.7 ConclusionReferences
12.1 Introduction12.2 Background12.3 Predictive Analytics12.4 Deep Learning Predictive Analysis Applications12.5 Discussion12.6 ConclusionReferences
13.1 Introduction13.2 Activities of Daily Living and Behavior Analysis13.3 Intelligent Home Architecture13.4 Methodology13.5 Senior Analytics Care Model13.6 Results and Discussions13.7 ConclusionNomenclatureReferences
14.1 Introduction14.2 Related Work14.3 Existing System14.4 Proposed System14.5 Results and Discussion14.6 ConclusionReferences
15.1 Introduction15.2 Related Work15.3 Development of Personalized Medicine Using Deep Learning: A New Revolution in Healthcare Industry15.4 Deep Learning Applications in Precision Medicine15.5 Deep Learning for Medical Imaging15.6 Drug Discovery and Development: A Promise Fulfilled by Deep Learning Technology15.7 Application Areas of Deep Learning in Healthcare15.8 Privacy Issues Arising With the Usage of Deep Learning in Healthcare15.9 Challenges and Opportunities in Healthcare Using Deep Learning15.10 Conclusion and Future ScopeReferences
16.1 Introduction16.2 Regularization in Machine Learning16.3 Convexity Principles16.4 Conclusion and DiscussionReferences
17.1 Introduction17.2 Machine Learning and Deep Learning Framework17.3 Challenges and Opportunities17.4 Clinical Databases—Electronic Health Records17.5 Data Analytics Models—Classifiers and Clusters17.6 Deep Learning Approaches and Association Predictions17.7 Conclusion17.8 ApplicationsReferences
18.1 Introduction18.2 Evolution of Machine Learning and Deep Learning18.3 The Forefront of Machine Learning Technology18.4 The Challenges Facing Machine Learning and Deep Learning18.5 Possibilities With Machine Learning and Deep Learning18.6 Potential Limitations of Machine Learning and Deep Learning18.7 ConclusionAcknowledgementContribution/DisclosureReferences

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Part 4DEEP LEARNING - IMPORTANCE AND CHALLENGES FOR OTHER SECTORS

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Publisher Resources

ISBN: 9781119785729Purchase Link