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.
Author(s): Parma Nand, Vishal Jain, Dac-Nhuong Le, Jyotir Moy Chatterjee, Ramani Kannan, Abhishek S. Verma
Publisher: Bentham Science Publishers
Year: 2023
Language: English
Pages: 129
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
INTRODUCTION
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
CONCLUSION
REFERENCES
Role of Blockchain in Healthcare Sector
Sheik Abdullah Abbas1,*, Karthikeyan Jothikumar2 and Arif Ansari3
INTRODUCTION
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
REFERENCES
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
INTRODUCTION
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
Convolutional Neural Network
VGG16 and ResNet50
GUI
CONCLUSION AND FUTURE WORK
NOTES
REFERENCES
A Robust Model for Optimum Medical Image Contrast Enhancement and Tumor Screening
Monika Agarwal1, Geeta Rani2,*, Vijaypal Singh Dhaka2 and Nitesh Pradhan3
INTRODUCTION
LITERATURE REVIEW
PROPOSED MODEL
Dataset
Image Pre-Processing
Features Extraction
Tumor Detection
RESULTS AND DISCUSSION
FUTURE SCOPE
CONCLUSION
REFERENCES
Subject Index
Back Cover
