Deep Learning for Healthcare Services
Description
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.
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Content
- 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
