Deep Learning Techniques for Biomedical and Health Informatics

$19.99

Download Deep Learning Techniques for Biomedical and Health Informatics written by Basant Agarwal, Valentina E. Balas, Lakhmi C. Jain, Ramesh Chandra Poonia, Manisha Sharma in PDF format. This book is under the category Health and bearing the isbn/isbn13 number 0128190612; 0128190620/9780128190616/ 9780128190623. You may reffer the table below for additional details of the book.

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Specifications

book-author

Basant Agarwal, Valentina E. Balas, Lakhmi C. Jain, Ramesh Chandra Poonia, Manisha Sharma

publisher

Academic Press

file-type

PDF

pages

625 pages

language

English

asin

B083QBPJF1

isbn10

0128190612; 0128190620

isbn13

9780128190616/ 9780128190623


Book Description

Deep Learning Techniques for Biomedical and Health Informatics (PDF) provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. This ebook covers not only the best-performing methods; it also presents implementation methods. The ebook includes all the prerequisite methodologies in every chapter so that new practitioners and researchers will find it very useful. Chapters go from basic methodology to advanced methods; including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Electronic Health Records; Biomedical Engineering; and medical image processing.

  • Provides detailed coverage of Deep Learning for medical image processing; including brain image analysis; brain tumor segmentation in MRI imaging; optimizing medical big data; and the future of biomedical image analysis
  • Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics; including clinical decision support systems; Deep Learning for drug discovery; prediction and monitoring; disease diagnosis
  • Discusses Deep Learning applied to Electronic Health Records (EHR); including health data structures and management; natural language processing; deep patient similarity learning; and how to improve clinical decision-making

NOTE: This sale only includes the ebook Deep Learning Techniques for Biomedical and Health Informatics in PDF. No access codes included.

Additional information

book-author

Basant Agarwal, Valentina E. Balas, Lakhmi C. Jain, Ramesh Chandra Poonia, Manisha Sharma

publisher

Academic Press

file-type

PDF

pages

625 pages

language

English

asin

B083QBPJF1

isbn10

0128190612; 0128190620

isbn13

9780128190616/ 9780128190623

Table of contents


Table of contents :
Front Matter
Copyright
Contributors
Unified neural architecture for drug, disease, and clinical entity recognition
Introduction
Method
Bi-directional long short-term memory
Model architecture
Features layer
Char BLSTM
Word BLSTM layer
CRF layer
Training and implementation
The benchmark tasks
Disease NER
Drug NER
Clinical NER
Results and discussion
Experiment design
Baseline methods
Comparison with baseline
Comparison with other methods
Disease NER
Drug NER
Feature ablation study
Effects of CRF and BLSTM
Effects of using fixed word embedding
Effect of size of the training data
Analysis of learned word embeddings
Error analysis
Conclusion
References
Simulation on real time monitoring for user healthcare information
Introduction
Background
Challenges
Objectives
Scope
Motivation
Organization
Literature review
Past researches
Proposed model development
Design framework
Procedures
Performance analysis
Benefits of proposed model
Experimental observations
Server-side working environment
Privacy policy
Experimental comparisons
Cost analysis
Real-time server-based observations
Scalability analysis
Monte Carlo simulation-based analysis
Simulation of derived framework for load testing based on real-time data
Novelty of proposed model
Conclusion
Acknowledgments
References
Multimodality medical image retrieval using convolutional neural network
Introduction
Need for medical image retrieval
Machine learning-convolutional neural network
General architecture of CNN
Convolutional neural network
Convolutional layer
Pooling layers
ReLu layer
Softmax layer
CBMIR methodology
Medical image database
LeNet and AlexNet
LeNet-5
AlexNet
Training of CNN architectures for classification
Optimizing the training parameters for CNN learning
Training implementation
LeNet model training
AlexNet model training
Medical image retrieval results and discussion
Image retrieval by similarity metrics
Image retrieval performance metrics
Retrieval results: LeNet
Retrieval results: AlexNet
Summary and conclusion
References
Further reading
A systematic approach for identification of tumor regions in the human brain through HARIS algorithm
Introduction
The intent of this chapter
Image enhancement and preprocessing
Adaptive contourlet transform
Image preprocessing for skull removal through structural augmentation
HARIS algorithm
HARIS algorithm objective function-I
HARIS algorithm objective function-II
Workflow of HARIS algorithm
Experimental analysis and results
Conclusion
Future scope
References
Further reading
Development of a fuzzy decision support system to deal with uncertainties in working posture analysis using rapid upper li …
Introduction
RULA method
Uncertainties occur in analyzing the working posture using RULA
Research methodology
MFIS
Development of fuzzy DSS
Arm and Wrist analysis
Step 1: Computation of Total Upper arm score
Step 1a: Calculation of Upper arm score
Step 1b: Adjustment score
Step 2: Computation of Total Lower arm score
Step 2a: Calculation of Lower arm score
Step 2b: Adjustment score
Step 3: Computation of Total Wrist score
Step 3a: Calculation of Wrist score
Step 3b: Adjustment score
Step 4: Wrist twist score
Step 5: Evaluation of Posture score A
Step 6: Computation of Muscle use score
Step 7: Evaluation of Force/load score
Step 8: Calculation of Wrist and Arm score
Neck, trunk, and leg analysis
Step 9: Computation of Total Neck score
Step 9a: Calculation of Neck score
Step 9b: Adjustment score
Step 10: Computation of Total Trunk score
Step 10a: Calculation of Trunk score
Step 10b: Adjustment score
Step 11: Computation of Leg score
Step 12: Evaluation of Posture score B
Step 13: Evaluation of Neck, Trunk, and Leg score
Posture analysis
Step 14: Evaluation of RULA score
Step 15: Identifying the Action level
Selection of most suitable operations associated with the proposed fuzzy DSS using MFIS
Analysis of postures of the female workers engaged in Sal leaf plate-making units: A case study
Results and discussion
Arm and Wrist analysis
Neck, trunk, and leg analysis
Posture analysis
Conclusions
Acknowledgments
References
Short PCG classification based on deep learning
Introduction
Heart sound analysis
Materials and methods
Related works
Limitation of segmentation
Database
Overall system design
Preprocessing
Continuous wavelet transform
Convolutional neural network
Convolutional layer
Pooling layer
Fully connected layer
CNN-based automatic prediction
GoogleNet
Training using GoogleNet
Performance parameter
Result
For analyzing individual datasets
For analyzing whole datasets
Discussion
Conclusion
References
Development of a laboratory medical algorithm for simultaneous detection and counting of erythrocytes and leukocytes in di …
Introduction
Blood cells and blood count
Manual hemogram
Automated hemogram
Digital image processing
Hough transform
Review
Materials and methods
Results and discussion
Future research directions
Conclusion
Acknowledgments
References
Deep learning techniques for optimizing medical big data
Relationship between deep learning and big data
Roles of deep learning and big data in medicine
What makes deep learning and big data necessary in medicine?
How are deep learning and big data changing the medicine industry?
Examples and application of machine learning in medicine
Disease identification/diagnosis
Personalized treatment
Drug discovery
Clinical trial research
Smart electronic health records
Medical big data promise and challenges
Promises and challenges
Data aggregation challenges
Policy and process challenges
Management challenges
Cloud storage
Data accommodation
Data personnel
Data nature
Technology incorporation
Medical big data techniques and tools
Batch processing technique and tools
Apache Hadoop
Dryad
Talend Open Studio
Apache Mahout
Pentaho
Stream processing tools
Storm
Splunk
Apache Kafka
Interactive analysis tools
Google Dremel
Apache Drilling
Existing optimization techniques for medical big data
Big data optimization tools for medicine
Sonata
ECL-Watch
Turbo
Other big data optimization tools
Analyzing big data in precision medicine
Subtyping and biomarker discovery
Drug repurposing and personalized treatment
Biomedical data
The increasing number of samples
Increasing heterogeneity of captured data
Deep learning in medicine
Computational methods
Disease subtyping and biomarker discovery
Drug repurposing and personalized treatments
Conclusion
References
Further reading
Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks
Introduction to simulation for biomedical signals and images
Deep learning for classification of biomedical signals and images
Supervised machine learning
Deep learning
Artificial neural networks
Convolutional neural networks
Deep learning requires good data
Labeled biomedical image data is difficult to obtain
Simulation of biological images and signals
Simulation can generate large amounts of labeled data
System modeling
Monte Carlo methods
Markov chain
Differences between synthetic data and real data
Addressing differences between simulated and real data
Reality gap bridging techniques
System identification
Importance sampling for Monte Carlo simulations
Adversarial networks
Data augmentation
Classification of optical coherence tomography images in heart tissues
Prediction model example: Classification of OCT images from heart tissues
Simulation to generate synthetic OCT images
System model example: Interferometry system for OCT
System model example: Physics of light propagation through biological tissue
Optical characterization of biological tissue
Free path
Scattering
Reflection
Absorption and termination of the photon packet
Class I and II paths
Coordinate systems
Verification of simulation results
Bridging the reality gap
Importance sampling
Generative adversarial network (GAN) for system identification
Predictive model training and test process
Data collection
Verification of predictive model results
Evaluation of predictive model(s)
Conclusion
References
Deep learning-based histopathological image analysis for automated detection and staging of melanoma
Introduction
Data description
Melanoma detection
Epidermis region identification
CNN-based nuclei segmentation
CNN architecture
CNN training
Nuclei classification
Feature extraction
Feature classification
Results and discussions
Segmentation performance
Nuclei classification performance
Cell proliferation index calculation
Lymph node segmentation
Melanoma region identification
CNN-based Nuclei Segmentation and Classification
PI calculation
Results and discussion
Conclusions
References
Potential proposal to improve data transmission in healthcare systems
Introduction
Telecommunications channels
Discrete events
Scientific grounding
Proposal and objectives
Methodology
Precoding bit
Signal validation by DQPSK modulation
Results
Discussion
Conclusion
References
Further reading
Transferable approach for cardiac disease classification using deep learning
Introduction
Proposed work
Dataset description
Arrhythmia
Myocardial infarction
Atrial fibrillation
Methodology
Background
Recurrent neural network
Long short-term memory
Gated recurrent unit
Residual convolutional neural network
Classical machine learning algorithms
Feature extraction
Classification algorithms
Network architecture
Recurrent networks
Residual convolution neural network
Experimental results
Train/test split
Hyperparameters
Evaluation metrics
Transferable approach for arrhythmia classification
Transferable approach for myocardial infarction classification
Transferable approach for atrial fibrillation classification
Comparison of the performance for the proposed method against the existing benchmark results
Conclusion
References
Automated neuroscience decision support framework
Introduction
Psychophysiological measures
Neurological data preprocessing
Importance of data preprocessing
Data preprocessing techniques
Software application support for neuroimage processing
Related studies
Neuroscience decision support framework
System design and methodology
Datasets
Solution implementation
Solution evaluation
Discussion
Conclusion
References
Diabetes prediction using artificial neural network
Introduction
State of art
Designing and developing the ANN-based model
Dataset
Implementation
Experiments
Comparative analysis
Summary
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
W
Z

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