| Description |
1 online resource : illustrations (some color) |
| Series |
Healthcare technologies series ; 43 |
|
Healthcare technologies series ; 43.
|
| Contents |
Intro -- Title -- Copyright -- Contents -- About the Editors -- 1 COVID-19 detection in X-ray images using customized CNN model -- 1.1 Introduction -- 1.2 Related work -- 1.2.1 Key contributions and proposed work -- 1.3 Materials and methods -- 1.3.1 Feature extraction and selection -- 1.4 Results and discussion -- 1.5 Conclusion and future scope -- References -- 2 Introducing deep learning in medical diagnosis -- 2.1 Introduction -- 2.2 Literature survey -- 2.3 Overview of DL algorithms -- 2.3.1 Convolutional neural network -- 2.3.2 Recurrent neural network -- 2.3.3 Long short-term memory |
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2.3.4 Restricted Boltzmann machine -- 2.3.5 Deep belief networks -- 2.4 Proposed DL framework for neuro disease diagnosis -- 2.4.1 FAST-RCNN -- 2.4.2 Ten fully connected layer -- 2.5 Preprocessing of dataset -- 2.6 Implementation and results -- 2.7 Conclusion -- References -- 3 Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML) -- 3.1 Introduction -- 3.1.1 DoS and DDoS attacks -- 3.1.2 Man-in-the-middle (MitM) attack -- 3.1.3 Phishing and spear-phishing attacks -- 3.1.4 Password attack -- 3.1.5 Eavesdropping attack -- 3.1.6 Malware attack |
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3.2 Related work -- 3.3 Cloud computing -- 3.3.1 Machine learning -- 3.3.2 Exploratory data analysis -- 3.4 Results -- References -- 4 Classification methodologies in healthcare -- 4.1 Introduction -- 4.2 Classification algorithms -- 4.2.1 Statistical data -- 4.2.2 Discriminant analysis -- 4.2.3 Decision tree -- 4.2.4 K-nearest neighbor (KNN) -- 4.2.5 Logistic regression (LR) -- 4.2.6 Bayesian classifier -- 4.2.7 Support vector machine (SVM) -- 4.3 Parameter identification -- 4.3.1 Feature selection for classi cation -- 4.4 Real-time applications |
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4.4.1 Classification of patients based on medical record -- 4.4.2 Predictive analytics and diagnostic analytics based on medical records -- 4.4.3 Classification of diseases based on medical imaging -- 4.4.4 Mixed reality-based automation to help aid aging society -- 4.4.5 Tiny ML-based classification systems for medical gadgets -- 4.4.6 Classification systems for insurance claim management -- 4.4.7 Case study: Inspectra from Perceptra -- 4.4.8 Deep learning for beginners -- References -- 5 Introducing deep learning in medical domain -- 5.1 Introduction -- 5.1.1 DL in a nutshell |
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5.1.2 History of DL in the medical field -- 5.1.3 Benefits of DL in the medical domain -- 5.1.4 Challenges and obstacles of DL in the medical domain -- 5.1.5 Opportunities of DL in the medical field -- 5.2 DL applications in the medical domain -- 5.2.1 Drug discovery and medicine precision -- 5.2.2 Detection of diseases -- 5.2.3 Diagnosing patients -- 5.2.4 Healthcare administration -- 5.3 DL for medical image analysis -- 5.3.1 Medical image detection -- 5.3.2 Medical image recognition -- 5.3.3 Medical image segmentation -- 5.3.4 Medical image registration |
| Summary |
This book examines machine learning trends in predictive technology to solve real-time healthcare problems. By using real-time data inputs to build predictive models, this new technology can model disease progression, assist with interventions or predict patient outcomes |
| Notes |
5.3.5 Disease diagnosis and quantification |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Artificial intelligence -- Medical applications.
|
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Predictive analytics.
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Artificial intelligence -- Medical applications
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Predictive analytics
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| Form |
Electronic book
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| Author |
Kumar, Abhishek, 1989- editor.
|
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Dubey, Ashutosh Kumar, editor.
|
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Bhatia, Surbhi, 1988- editor.
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Kumar, Swarn Avinash, editor.
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Le, Dac-Nhuong, 1983- editor.
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| ISBN |
9781839535123 |
|
1839535121 |
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