Computational Analysis and Deep Learning for Medical Care (eBook, PDF)

Principles, Methods, and Applications
Redaktion: Tyagi, Amit Kumar

• Format: PDF

Produktbeschreibung

The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 528
• Erscheinungstermin: 2. August 2021
• Englisch
• ISBN-13: 9781119785743
• Artikelnr.: 62420594

Autorenporträt

Amit Kumar Tyagi is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber physical systems and computer vision.

Inhaltsangabe

Preface xix

Part I: Deep Learning and Its Models 1

1 CNN: A Review of Models, Application of IVD Segmentation 3
Leena Silvoster M. and R. Mathusoothana S. Kumar

1.1 Introduction 4

1.2 Various CNN Models 4

1.2.1 LeNet-5 4

1.2.2 AlexNet 7

1.2.3 ZFNet 8

1.2.4 VGGNet 10

1.2.5 GoogLeNet 12

1.2.6 ResNet 16

1.2.7 ResNeXt 21

1.2.8 SE-ResNet 24

1.2.9 DenseNet 24

1.2.10 MobileNets 25

1.3 Application of CNN to IVD Detection 26

1.4 Comparison With State-of-the-Art Segmentation Approaches for Spine T2W Images 28

1.5 Conclusion 28

References 33

2 Location-Aware Keyword Query Suggestion Techniques With Artificial Intelligence Perspective 35
R. Ravinder Reddy, C. Vaishnavi, Ch. Mamatha and S. Ananthakumaran

2.1 Introduction 36

2.2 Related Work 39

2.3 Artificial Intelligence Perspective 41

2.3.1 Keyword Query Suggestion 42

2.3.1.1 Random Walk-Based Approaches 42

2.3.1.2 Cluster-Based Approaches 42

2.3.1.3 Learning to Rank Approaches 43

2.3.2 User Preference From Log 43

2.3.3 Location-Aware Keyword Query Suggestion 44

2.3.4 Enhancement With AI Perspective 44

2.3.4.1 Case Study 45

2.4 Architecture 46

2.4.1 Distance Measures 47

2.5 Conclusion 49

References 49

3 Identification of a Suitable Transfer Learning Architecture for Classification: A Case Study with Liver Tumors 53
B. Lakshmi Priya, K. Jayanthi, Biju Pottakkat and G. Ramkumar

3.1 Introduction 54

3.2 Related Works 56

3.3 Convolutional Neural Networks 58

3.3.1 Feature Learning in CNNs 59

3.3.2 Classification in CNNs 60

3.4 Transfer Learning 61

3.4.1 AlexNet 61

3.4.2 GoogLeNet 62

3.4.3 Residual Networks 63

3.4.3.1 ResNet-18 65

3.4.3.2 ResNet-50 65

3.5 System Model 66

3.6 Results and Discussions 67

3.6.1 Dataset 67

3.6.2 Assessment of Transfer Learning Architectures 67

3.7 Conclusion 73

References 74

4 Optimization and Deep Learning-Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images 79
Suresh Kumar K., Sundaresan S., Nishanth R. and Ananth Kumar T.

4.1 Introduction 80

4.2 Related Works 82

4.3 Proposed Method 85

4.3.1 Input Dataset 86

4.3.2 Pre-Processing 86

4.3.3 Combination of DCNN and CFML 86

4.3.4 Fine Tuning and Optimization 88

4.3.5 Feature Extraction 89

4.3.6 Localization of Abnormalities in MRI and CT Scanned Images 90

4.4 Results and Discussion 92

4.4.1 Metric Learning 92

4.4.2 Comparison of the Various Models for Image Retrieval 92

4.4.3 Precision vs. Recall Parameters Estimation for the CBIR 93

4.4.4 Convolutional Neural Networks-Based Landmark Localization 96

4.5 Conclusion 104

References 104

Part II: Applications of Deep Learning 107

5 Deep Learning for Clinical and Health Informatics 109
Amit Kumar Tyagi and Meghna Mannoj Nair

5.1 Introduction 110

5.1.1 Deep Learning Over Machine Learning 111

5.2 Related Work 113

5.3 Motivation 115

5.4 Scope of the Work in Past, Present, and Future 115

5.5 Deep Learning Tools, Methods Available for Clinical, and Health Informatics 117

5.6 Deep Learning: Not-So-Near Future in Biomedical Imaging 119