Deep Learning for Healthcare Services / edited by Parma Nand [and five others].

Format:

Book

Author/Creator:

Nand, Parma, Author.

Contributor:

Nand, Parma (Computer scientist), editor.

Series:

IoT and Big Data Analytics Series

IoT and Big Data Analytics Series ; Volume 2

Language:

English

Subjects (All):

Deep learning (Machine learning).

Medical care--Technological innovations.

Medical care.

Physical Description:

1 online resource (129 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Pte. Ltd., 2023.

Summary:

This book highlights the applications of deep learning algorithms in implementing big data and IoT enabled smart solutions to treat and care for terminally ill patients. It presents 5 concise chapters showing how these technologies can empower the conventional doctor patient relationship in a more dynamic, transparent, and personalized manner. The key topics covered in this book include: - The Role of Deep Learning in Healthcare Industry: Limitations - Generative Adversarial Networks for Deep Learning in Healthcare - The Role of Blockchain in the Healthcare Sector - Brain Tumor Detection Based on Different Deep Neural Networks Key features include a thorough, research-based overview of technologies that can assist deep learning models in the healthcare sector, including architecture and industrial scope. The book also presents a robust image processing model for brain tumor screening. Through this book, the editors have attempted to combine numerous compelling views, guidelines and frameworks. Healthcare industry professionals will understand how Deep Learning can improve health care service delivery.

Contents:

Cover

Title

Copyright

End User License Agreement

Content

Preface

List of Contributors

Role of Deep Learning in Healthcare Industry: Limitations, Challenges and Future Scope

Mandeep Singh1,*, Megha Gupta2, Anupam Sharma3, Parita Jain4 and Puneet Kumar Aggarwal5

INTRODUCTION

A Framework of Deep Learning

LITERATURE REVIEW

E-Health Records by Deep Learning

Medical Images by Deep Learning

Genomics by Deep Learning

Use of Mobiles by Deep Learning

FROM PERCEPTRON TO DEEP LEARNING

Recurrent Neural Network (RNN)

Convolutional Neural Network (CNN)

Boltzmann Machine Technique

Auto-Encoder and Deep Auto-Encoder

Hardware/ Software-Based Implementation

DEEP LEARNING IN HEALTHCARE: FUTURE SCOPE, LIMITATIONS, AND CHALLENGES

CONCLUSION

REFERENCES

Generative Adversarial Networks for Deep Learning in Healthcare: Architecture, Applications and Challenges

Shankey Garg1,* and Pradeep Singh1

DEEP LEARNING

The Transition from Machine Learning to DL

Deep Feed-forward Networks

Restricted Boltzmann Machines

Deep Belief Networks

Convolutional Neural Networks

Recurrent Neural Networks

GENERATIVE ADVERSARIAL NETWORKS

GANs Architectures

Deep Convolutional GAN(DCGAN)

InfoGAN

Conditional GANs

Auto Encoder GANs

Cycle GANs

GANs Training Tricks

Objective Function-Based Improvement

Skills Based Techniques

Other Miscellaneous Techniques

STATE-OF-THE-ART APPLICATIONS OF GANS

Image-Based Applications

Sequential Data Based Applications

Other Applications

FUTURE CHALLENGES

Role of Blockchain in Healthcare Sector

Sheik Abdullah Abbas1,*, Karthikeyan Jothikumar2 and Arif Ansari3

FEATURES OF BLOCKCHAIN

DATA MANAGEMENT AND ITS SERVICES (TRADITIONAL VS DISTRIBUTED).

DATA DECENTRALIZATION AND ITS DISTRIBUTION

ASSET MANAGEMENT

ANALYTICS

Analytics Process Model

Analytic Model Requirements

IMMUTABILITY FOR BIOMEDICAL APPLIANCES IN BLOCKCHAIN

SECURITY AND PRIVACY

BLOCKCHAIN IN BIOMEDICINE AND ITS APPLICATIONS

Case Study

CONCLUSION AND FUTURE WORK

Brain Tumor Detection Based on Different Deep Neural Networks - A Comparison Study

Shrividhiya Gaikwad1, Srujana Kanchisamudram Seshagiribabu1, Sukruta Nagraj Kashyap1, Chitrapadi Gururaj1,* and Induja Kanchisamudram Seshagiribabu2

RELATED WORK

APPROACH

Dataset

Data Pre-Processing

Data Augmentation

Contouring

Transfer Learning

MODELS USED IN THE COMPARISON STUDY

Convolutional Neural Network

Input Layer

Convolution Layer

Activation Layer

Pooling Layer

Fully Connected Layer

Output

VGG 16

ResNet 50

EVALUATION PARAMETERS

RESULTS AND DISCUSSION

VGG16 and ResNet50

GUI

NOTES

A Robust Model for Optimum Medical Image Contrast Enhancement and Tumor Screening

Monika Agarwal1, Geeta Rani2,*, Vijaypal Singh Dhaka2 and Nitesh Pradhan3

PROPOSED MODEL

Image Pre-Processing

Features Extraction

Tumor Detection

FUTURE SCOPE

Subject Index

Back Cover.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9789815080230

9815080237

OCLC:

1394121242