Computational Analysis and Deep Learning for Medical Care E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 686

Veröffentlichungsjahr: 2021

Beschreibung

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.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

Part 1: Deep Learning and Its Models

1 CNN: A Review of Models, Application of IVD Segmentation

1.1 Introduction

1.2 Various CNN Models

1.3 Application of CNN to IVD Detection

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

1.5 Conclusion

References

2 Location-Aware Keyword Query Suggestion Techniques With Artificial Intelligence Perspective

2.1 Introduction

2.2 Related Work

2.3 Artificial Intelligence Perspective

2.4 Architecture

2.5 Conclusion

References

3 Identification of a Suitable Transfer Learning Architecture for Classification: A Case Study with Liver Tumors

3.1 Introduction

3.2 Related Works

3.3 Convolutional Neural Networks

3.4 Transfer Learning

3.5 System Model

3.6 Results and Discussions

3.7 Conclusion

References

4 Optimization and Deep Learning-Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images

4.1 Introduction

4.2 Related Works

4.3 Proposed Method

4.4 Results and Discussion

4.5 Conclusion

References

Part 2: Applications of Deep Learning

5 Deep Learning for Clinical and Health Informatics

5.1 Introduction

5.2 Related Work

5.3 Motivation

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

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

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

5.7 Challenges Faced Toward Deep Learning Using in Biomedical Imaging

5.8 Open Research Issues and Future Research Directions in Biomedical Imaging (Healthcare Informatics)

5.9 Conclusion

References

6 Biomedical Image Segmentation by Deep Learning Methods

6.1 Introduction

6.2 Overview of Deep Learning Algorithms

6.3 Other Deep Learning Architecture

6.4 Biomedical Image Segmentation

6.5 Conclusion

References

7 Multi-Lingual Handwritten Character Recognition Using Deep Learning

7.1 Introduction

7.2 Related Works

7.3 Materials and Methods

7.4 Experiments and Results

7.5 Conclusion

References

8 Disease Detection Platform Using Image Processing Through OpenCV

8.1 Introduction

8.2 Problem Statement

8.3 Conclusion

8.4 Summary

References

9 Computer-Aided Diagnosis of Liver Fibrosis in Hepatitis Patients Using Convolutional Neural Network

9.1 Introduction

9.2 Overview of System

9.3 Methodology

9.4 Performance and Analysis

9.5 Experimental Results

9.6 Conclusion and Future Scope

References

Part 3: Future Deep Learning Models

10 Lung Cancer Prediction in Deep Learning Perspective

10.1 Introduction

10.2 Machine Learning and Its Application

10.3 Related Work

10.4 Why Deep Learning on Top of Machine Learning?

10.5 How is Deep Learning Used for Prediction of Lungs Cancer?

10.6 Conclusion

References

11 Lesion Detection and Classification for Breast Cancer Diagnosis Based on Deep CNNs from Digital Mammographic Data

11.1 Introduction

11.2 Background

11.3 Methods

11.4 Application of Deep CNN for Mammography

11.5 System Model and Results

11.6 Research Challenges and Discussion on Future Directions

11.7 Conclusion

References

12 Health Prediction Analytics Using Deep Learning Methods and Applications

12.1 Introduction

12.2 Background

12.3 Predictive Analytics

12.4 Deep Learning Predictive Analysis Applications

12.5 Discussion

12.6 Conclusion

References

13 Ambient-Assisted Living of Disabled Elderly in an Intelligent Home Using Behavior Prediction—A Reliable Deep Learning Prediction System

13.1 Introduction

13.2 Activities of Daily Living and Behavior Analysis

13.3 Intelligent Home Architecture

13.4 Methodology

13.5 Senior Analytics Care Model

13.6 Results and Discussions

13.7 Conclusion

Nomenclature

References

14 Early Diagnosis Tool for Alzheimer’s Disease Using 3D Slicer

14.1 Introduction

14.2 Related Work

14.3 Existing System

14.4 Proposed System

14.5 Results and Discussion

14.6 Conclusion

References

Part 4: Deep Learning - Importance and Challenges for Other Sectors

15 Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities

15.1 Introduction

15.2 Related Work

15.3 Development of Personalized Medicine Using Deep Learning: A New Revolution in Healthcare Industry

15.4 Deep Learning Applications in Precision Medicine

15.5 Deep Learning for Medical Imaging

15.6 Drug Discovery and Development: A Promise Fulfilled by Deep Learning Technology

15.7 Application Areas of Deep Learning in Healthcare

15.8 Privacy Issues Arising With the Usage of Deep Learning in Healthcare

15.9 Challenges and Opportunities in Healthcare Using Deep Learning

15.10 Conclusion and Future Scope

References

16 A Perspective Analysis of Regularization and Optimization Techniques in Machine Learning

16.1 Introduction

16.2 Regularization in Machine Learning

16.3 Convexity Principles

16.4 Conclusion and Discussion

References

17 Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges

17.1 Introduction

17.2 Machine Learning and Deep Learning Framework

17.3 Challenges and Opportunities

17.4 Clinical Databases—Electronic Health Records

17.5 Data Analytics Models—Classifiers and Clusters

17.6 Deep Learning Approaches and Association Predictions

17.7 Conclusion

17.8 Applications

References

18 Machine Learning and Deep Learning: Open Issues and Future Research Directions for the Next 10 Years

18.1 Introduction

18.2 Evolution of Machine Learning and Deep Learning

18.3 The Forefront of Machine Learning Technology

18.4 The Challenges Facing Machine Learning and Deep Learning

18.5 Possibilities With Machine Learning and Deep Learning

18.6 Potential Limitations of Machine Learning and Deep Learning

18.7 Conclusion

Acknowledgement

Contribution/Disclosure

References

Index

List of Illustrations

Chapter 1

Figure 1.1 Architecture of LeNet-5.

Figure 1.2 Architecture of AlexNet.

Figure 1.3 Architecture of ZFNet.

Figure 1.4 Architecture of VGG-16.

Figure 1.5 Inception module.

Figure 1.6 Architecture of GoogleNet.

Figure 1.7 (a) A residual block.

Figure 1.8 Architecture of ResNeXt.

Figure 1.9 Architecture of SE-ResNet.

Figure 1.10 Architecture of DenseNet.

Figure 1.11 Architecture of MobileNets.

Chapter 2

Figure 2.1 General architecture of a search engine.

Figure 2.2 The increased mobile users.

Figure 2.3 AI-powered location-based system.

Figure 2.4 Architecture diagram for querying.

Chapter 3

Figure 3.1 Phases of CECT images (1: normal liver; 2: tumor within liver; 3: sto...

Figure 3.2 Architecture of convolutional neural network.

Figure 3.3 AlexNet architecture.

Figure 3.4 GoogLeNet architecture.

Figure 3.5 Residual learning—building block.

Figure 3.6 Architecture of ResNet-18.

Figure 3.7 System model for case study on liver tumor diagnosis.

Figure 3.8 Output of bidirectional region growing segmentation algorithm: (a) in...

Figure 3.9 HA Phase Liver CT images: (a) normal liver; (b) HCC; (c) hemangioma; ...

Figure 3.10 Training progress for AlexNet.

Figure 3.11 Training progress for GoogLeNet.

Figure 3.12 Training progress for ResNet-18.

Figure 3.13 Training progress for ResNet-50.

Chapter 4

Figure 4.1 Proposed system for image retrieval.

Figure 4.2 Schematic of the deep convolutional neural networks.

Figure 4.3 Proposed feature extraction system.

Figure 4.4 Proposed model for the localization of the abnormalities.

Figure 4.5 Graph for the retrieval performance of the metric learning for VGG19.

Figure 4.6 PR values for state of art ConvNet model for CT images.

Figure 4.7 PR values for state of art CNN model for CT images.

Figure 4.8 Proposed system—PR values for the CT images.

Figure 4.9 PR values for proposed content-based image retrieval.

Figure 4.10 Graph for loss function of proposed deep regression networks for tra...

Figure 4.11 Graph for loss function of proposed deep regression networks for val...

Chapter 6

Figure 5.1 Different informatics in healthcare [28].

Chapter 6

Figure 6.1 CT image reconstruction (past, present, and future) [3].

Figure 6.2 (a) Classic machine learning algorithm, (b) Deep learning algorithm.

Figure 6.3 Traditional neural network.

Figure 6.4 Convolutional Neural Network.

Figure 6.5 Psoriasis images [2].

Figure 6.6 Restricted Boltzmann Machine.

Figure 6.7 Autoencoder architecture with vector and image inputs [1].

Figure 6.8 Image of chest x-ray [60].

Figure 6.9 Regular thoracic disease identified in chest x-rays [23].

Figure 6.10 MRI of human brain [4].

Chapter 7

Figure 7.1 Architecture of the proposed approach.

Figure 7.2 Sample Math dataset (including English characters).

Figure 7.3 Sample Bangla dataset (including Bangla numeric).

Figure 7.4 Sample Devanagari dataset (including Hindi numeric).

Figure 7.5 Dataset distribution for English dataset.

Figure 7.6 Dataset distribution for Hindi dataset.

Figure 7.7 Dataset distribution for Bangla dataset.

Figure 7.8 Dataset distribution for Math Symbol dataset.

Figure 7.9 Dataset distribution.

Figure 7.10 Precision-recall curve on English dataset.

Figure 7.11 ROC curve on English dataset.

Figure 7.12 Precision-recall curve on Hindi dataset.

Figure 7.13 ROC curve on Hindi dataset.

Figure 7.14 Precision-recall curve on Bangla dataset.

Figure 7.15 ROC curve on Bangla dataset.

Figure 7.16 Precision-recall curve on Math Symbol dataset.

Figure 7.17 ROC curve on Math symbol dataset.

Figure 7.18 Precision-recall curve of the proposed model.

Figure 7.19 ROC curve of the proposed model.

Chapter 8

Figure 8.1 Eye image dissection [34].

Figure 8.2 Cataract algorithm [10].

Figure 8.3 Pre-processing algorithm [48].

Figure 8.4 Pre-processing analysis [39].

Figure 8.5 Morphologically opened [39].

Figure 8.6 Finding circles [40].

Figure 8.7 Iris contour separation [40].

Figure 8.8 Image inversion [41].

Figure 8.9 Iris detection [41].

Figure 8.10 Cataract detection [41].

Figure 8.11 Healthy eye vs. retinoblastoma [33].

Figure 8.12 Unilateral retinoblastoma [18].

Figure 8.13 Bilateral retinoblastoma [19].

Figure 8.14 Classification of stages of skin cancer [20].

Figure 8.15 Eye cancer detection algorithm.

Figure 8.16 Sample test cases.

Figure 8.17 Actual working of the eye cancer detection algorithm.

Figure 8.18 Melanoma example [27].

Figure 8.19 Melanoma detection algorithm.

Figure 8.20 Asymmetry analysis.

Figure 8.21 Border analysis.

Figure 8.22 Color analysis.

Figure 8.23 Diameter analysis.

Figure 8.24 Completed detailed algorithm.

Chapter 9

Figure 9.1 Basic overview of a proposed computer-aided system.

Figure 9.2 Block diagram of the proposed system for finding out liver fibrosis.

Figure 9.3 Block diagram representing different pre-processing stages in liver f...

Figure 9.4 Flow chart showing student’s t test.

Figure 9.5 Diagram showing SegNet architecture for convolutional encoder and dec...

Figure 9.6 Basic block diagram of VGG-16 architecture.

Figure 9.7 Flow chart showing SegNet working process for classifying liver fibro...

Figure 9.8 Overall process of the CNN of the system.

Figure 9.9 The stages in identifying liver fibrosis by using Conventional Neural...

Figure 9.10 Multi-layer neural network architecture for a CAD system for diagnos...

Figure 9.11 Graphical representation of Support Vector Machine.

Figure 9.12 Experimental analysis graph for different classifier in terms of acc...

Chapter 10

Figure 10.1 Block diagram of machine learning.

Figure 10.2 Machine learning algorithm.

Figure 10.3 Structure of deep learning.

Figure 10.4 Architecture of DNN.

Figure 10.5 Architecture of CNN.

Figure 10.6 System architecture.

Figure 10.7 Image before histogram equalization.

Figure 10.8 Image after histogram equalization.

Figure 10.9 Edge detection.

Figure 10.10 Edge segmented image.

Figure 10.11 Total cases.

Figure 10.12 Result comparison.

Chapter 11

Figure 11.1 Breast cancer incidence rates worldwide (source: International Agenc...

Figure 11.2 Images from MIAS database showing normal, benign, malignant mammogra...

Figure 11.3 Image depicting noise in a mammogram.

Figure 11.4 Architecture of CNN.

Figure 11.5 A complete representation of all the operation that take place at va...

Figure 11.6 An image depicting Pouter, Plesion, and Pbreast in a mammogram.

Figure 11.7 The figure depicts two images: (a) mammogram with a malignant mass a...

Figure 11.8 A figure depicting the various components of a breast as identified ...

Figure 11.9 An illustration of how a mammogram image having tumor is segmented t...

Figure 11.10 A schematic representation of classification procedure of CNN.

Figure 11.11 A schematic representation of classification procedure of CNN durin...

Figure 11.12 Proposed system model.

Figure 11.13 Flowchart for MIAS database and unannotated labeled images.

Figure 11.14 Image distribution for training model.

Figure 11.15 The graph shows the loss for the trained model on train and test da...

Figure 11.16 The graph shows the accuracy of the trained model for both test and...

Figure 11.17 Depiction of the confusion matrix for the trained CNN model.

Figure 11.18 Receiver operating characteristics of the trained model.

Figure 11.19 The image shows the summary of the CNN model.

Figure 11.20 Performance parameters of the trained model.

Figure 11.21 Prediction of one of the image collected from diagnostic center.

Chapter 12

Figure 12.1 Deep learning [14]. (a) A simple, multilayer deep neural network tha...

Figure 12.2 Flowchart of the model [25]. The orange icon indicates the dataset, ...

Figure 12.3 Evaluation result [25].

Figure 12.4 Deep learning techniques evaluation results [25].

Figure 12.5 Deep transfer learning–based screening system [38].

Figure 12.6 Classification result.

Figure 12.7 Regression result [45].

Figure 12.8 AE model of deep learning [47].

Figure 12.9 DBN for induction motor fault diagnosis [68].

Figure 12.10 CNN model for health monitoring [80].

Figure 12.11 RNN model for health monitoring [87].

Figure 12.12 Deep learning models usage.

Chapter 13

Figure 13.1 Intelligent home layout model.

Figure 13.2 Deep learning model in predicting behavior analysis.

Figure 13.3 Lifestyle-oriented context aware model.

Figure 13.4 Components for the identification, simulation, and detection of acti...

Figure 13.5 Prediction stages.

Figure 13.6 Analytics of event.

Figure 13.7 Prediction of activity duration.

Chapter 14

Figure 14.1 Comparison of normal and Alzheimer brain.

Figure 14.2 Proposed AD prediction system.

Figure 14.3 KNN classification.

Figure 14.4 SVM classification.

Figure 14.5 Load data in 3D slicer.

Figure 14.6 3D slicer visualization.

Figure 14.7 Normal patient MRI.

Figure 14.8 Alzheimer patient MRI.

Figure 14.9 Comparison of hippocampus region.

Figure 14.10 Accuracy of algorithms with baseline records.

Figure 14.11 Accuracy of algorithms with current records.

Figure 14.12 Comparison of without and with dice coefficient.

Chapter 15

Figure 15.1 U-Net architecture [19].

Figure 15.2 Architecture of the 3D-DCSRN model [29].

Figure 15.3 SMILES code for Cyclohexane and Acetaminophen [32].

Figure 15.4 Medical chatbot architecture [36].

Chapter 16

Figure 16.1 A classical perceptron.

Figure 16.2 Forward and backward paths on an ANN architecture.

Figure 16.3 A DNN architecture.

Figure 16.4 A DNN architecture for digit classification.

Figure 16.5 Underfit and overfit.

Figure 16.6 Functional mapping.

Figure 16.7 A generalized Tikhonov functional.

Figure 16.8 (a) With hidden layers (b) Dropping h2 and h5.

Figure 16.9 Image cropping as one of the features of data augmentation.

Figure 16.10 Early stopping criteria based on errors.

Figure 16.11 (a) Convex, (b) Non-convex.

Figure 16.12 (a) Affine (b) Convex function.

Figure 16.13 Workflow and an optimizer.

Figure 16.14 (a) Error (cost) function (b) Elliptical: Horizontal cross section.

Figure 16.15 Contour plot for a quadratic cost function with elliptical contours...

Figure 16.16 Gradients when steps are varying.

Figure 16.17 Local minima. (When the gradient ∇ of the partial derivatives is po...

Figure 16.18 Contour plot showing basins of attraction.

Figure 16.19 (a) Saddle point S. (b) Saddle point over a two-dimensional error s...

Figure 16.20 Local information encoded by the gradient usually does not support ...

Figure 16.21 Direction of gradient change.

Figure 16.22 Rolling ball and its trajectory.

Chapter 17

Figure 17.1 Artificial Neural Networks vs. Architecture of Deep Learning Model [...

Figure 17.2 Machine learning and deep learning techniques [4, 5].

Figure 17.3 Model of reinforcement learning (https://www.kdnuggets.com).

Figure 17.4 Data analytical model [5].

Figure 17.5 Support Vector Machine—classification approach [1].

Figure 17.6 Expected output of K-means clustering [1].

Figure 17.7 Output of mean shift clustering [2].

Figure 17.8 Genetic Signature–based Hierarchical Random Forest Cluster (G-HR Clu...

Figure 17.9 Artificial Neural Networks vs. Deep Learning Neural Networks.

Figure 17.10 Architecture of Convolution Neural Network.

Figure 17.11 Architecture of the Human Diseases Pattern Prediction Technique (EC...

Figure 17.12 Comparative analysis: processing time vs. classifiers.

Figure 17.13 Comparative analysis: memory usage vs. classifiers.

Figure 17.14 Comparative analysis: classification accuracy vs. classifiers.

Figure 17.15 Comparative analysis: sensitivity vs. classifiers.

Figure 17.16 Comparative analysis: specificity vs. classifiers.

Figure 17.17 Comparative analysis: FScore vs. classifiers.

Chapter 18

Figure 18.1 Deep Neural Network (DNN).

Figure 18.2 The evolution of machine learning techniques (year-wise).

List of Tables

Chapter 1

Table 1.1 Various parameters of the layers of LeNet.

Table 1.2 Every column indicates which feature map in S2 are combined by the uni...

Table 1.3 AlexNet layer details.

Table 1.4 Various parameters of ZFNet.

Table 1.5 Various parameters of VGG-16.

Table 1.6 Various parameters of GoogleNet.

Table 1.7 Various parameters of ResNet.

Table 1.8 Comparison of ResNet-50 and ResNext-50 (32 × 4d).

Table 1.9 Comparison of ResNet-50 and ResNext-50 and SE-ResNeXt-50 (32 × 4d).

Table 1.10 Comparison of DenseNet.

Table 1.11 Various parameters of MobileNets.

Table 1.12 State-of-art of spine segmentation approaches.

Chapter 2

Table 2.1 History of search engines.

Table 2.2 Three types of user refinement of queries.

Table 2.3 Different approaches for the query suggestion techniques.

Chapter 3

Table 3.1 Types of liver lesions.

Table 3.2 Dataset count.

Table 3.3 Hyperparameter settings for training.

Table 3.4 Confusion matrix for AlexNet.

Table 3.5 Confusion matrix for GoogLeNet.

Table 3.6 Confusion matrix for ResNet-18.

Table 3.7 Confusion matrix for ResNet-50.

Table 3.8 Comparison of classification accuracies.

Chapter 4

Table 4.1 Retrieval performance of metric learning for VGG19.

Table 4.2 Performance of retrieval techniques of the trained VGG19 among fine-tu...

Table 4.3 PR values of various models—a comparison for CT image retrieval.

Table 4.4 Recall vs. precision for proposed content-based image retrieval.

Table 4.5 Loss function of proposed deep regression networks for training datase...

Table 4.6 Loss function of proposed deep regression networks for validation data...

Table 4.7 Land mark details (identification rates vs. distance error) for the pr...

Table 4.8 Accuracy value of the proposed system.

Table 4.9 Accuracy of the retrieval methods compared with the metric learning–ba...

Chapter 6

Table 6.1 Definition of the abbreviations.

Chapter 7

Table 7.1 Performance of proposed models on English dataset.

Table 7.2 Performance of proposed model on Bangla dataset.

Table 7.3 Performance of proposed model on Math Symbol dataset.

Chapter 8

Table 8.1 ABCD factor for TDS value.

Table 8.2 Classify mole according to TDS value.

Chapter 9

Table 9.1 The confusion matrix for different classifier.

Table 9.2 Performance analysis of different classifiers: Random Forest, SVM, Naï...

Chapter 10

Table 10.1 Result analysis.

Chapter 11

Table 11.1 Comparison of different techniques and tumor.

Chapter 13

Table 13.1 Cognitive functions related with routine activities.

Table 13.2 Situation and design features.

Table 13.3 Accuracy of prediction.

Chapter 14

Table 14.1 Accuracy comparison and mean of algorithms with baseline records.

Table 14.2 Accuracy comparison and mean of algorithms with current records.

Chapter 15

Table 15.1 Variances of Convolutional Neural Network (CNN).

Table 15.2 Various issues challenges faced by researchers for using deep learnin...

Chapter 17

Table 17.1 Comparative analysis: classification accuracy for 10 datasets—analysi...

Chapter 18

Table 18.1 Comparison among data mining, machine learning, and deep learning.

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Begin Reading

Index

End User License Agreement

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Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Computational Analysis and Deep Learning for Medical Care

Principles, Methods, and Applications

Edited by

Amit Kumar Tyagi

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Preface

Due to recent technological developments and the integration of millions of Internet of Things (IoT)-connected devices, a large volume of data is being generated every day. This data, known as big data, is summed up by the 7 V’s—Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value. Efficient tools, models and algorithms are required to analyze this data in order to advance the development of applications in several sectors, including e-healthcare (i.e., for disease prediction) and satellites (i.e., for weather prediction) among others. In the case of data related to biomedical imaging, this analyzed data is very useful to doctors and their patients in making predictive and effective decisions when treating disease. The healthcare sector needs to rely on smart machines/devices to collect data; however, nowadays, these smart machines/devices are facing several critical issues, including security breaches, data leaks of private information, loss of trust, etc.

We are currently entering 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, our 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 will be 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 faced. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications.

Based on the above facts, some reliable deep learning and deep neural network models for healthcare applications are contained in this book on computational analysis and deep learning for medical care. These chapters are contributed by reputed authors; the importance of deep learning models is discussed along with the issues and challenges facing available current deep learning models. Also included are innovative deep learning algorithms/models for treating disease in the Medicare population. Finally, several research gaps are revealed in deep learning models for healthcare applications that will provide opportunities for several research communities.

In conclusion, we want to thank our God, family members, teachers, friends and last but not least, all our authors from the bottom of our hearts (including publisher) for helping us complete this book before the deadline. Really, kudos to all.

Amit Kumar Tyagi

Part 1DEEP LEARNING AND ITS MODELS