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Computational Analysis and Deep Learning for Medical Care

Principles, Methods, and Applications

Edited by Amit Kumat Tyagi
Series: Machine Learning in Biomedical Science and Healthcare Informatics
Copyright: 2021   |   Status: Published
ISBN: 9781119785729  |  Hardcover  |  
528 pages | 205 illustrations
Price: $225 USD
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One Line Description
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.

Audience
Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.

Description
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.

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Author / Editor Details
Amit Kumar Tyagi, PhD is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber physical systems, and computer vision.

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Table of Contents
Preface
Part I: Deep Learning and Its Models
1. CNN: A Review of Models, Application of IVD Segmentation
Leena Silvoster M. and R. Mathusoothana S. Kumar
1.1 Introduction
1.2 Various CNN Models
1.2.1 LeNet-5
1.2.2 AlexNet
1.2.3 ZFNet
1.2.4 VGGNet
1.2.5 GoogLeNet
1.2.6 ResNet
1.2.7 ResNeXt
1.2.8 SE-ResNet
1.2.9 DenseNet
1.2.10 MobileNets
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
R. Ravinder Reddy, C. Vaishnavi, Ch. Mamatha and S. Ananthakumaran
2.1 Introduction
2.2 Related Work
2.3 Artificial Intelligence Perspective
2.3.1 Keyword Query Suggestion
2.3.1.1 Random Walk–Based Approaches
2.3.1.2 Cluster-Based Approaches
2.3.1.3 Learning to Rank Approaches
2.3.2 User Preference From Log
2.3.3 Location-Aware Keyword Query Suggestion
2.3.4 Enhancement With AI Perspective
2.3.4.1 Case Study
2.4 Architecture
2.4.1 Distance Measures
2.5 Conclusion
References
3. Identification of a Suitable Transfer Learning Architecture for Classification: A Case Study with Liver Tumors
B. Lakshmi Priya, K. Jayanthi, Biju Pottakkat and G. Ramkumar
3.1 Introduction
3.2 Related Works
3.3 Convolutional Neural Networks
3.3.1 Feature Learning in CNNs
3.3.2 Classification in CNNs
3.4 Transfer Learning
3.4.1 AlexNet
3.4.2 GoogLeNet
3.4.3 Residual Networks
3.4.3.1 ResNet-18
3.4.3.2 ResNet-50
3.5 System Model
3.6 Results and Discussions
3.6.1 Dataset
3.6.2 Assessment of Transfer Learning Architectures
3.7 Conclusion
References
4. Optimization and Deep Learning-Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images
Suresh Kumar K., Sundaresan S., Nishanth R. and Ananth Kumar T.
4.1 Introduction
4.2 Related Works
4.3 Proposed Method
4.3.1 Input Dataset
4.3.2 Pre-Processing
4.3.3 Combination of DCNN and CFML
4.3.4 Fine Tuning and Optimization
4.3.5 Feature Extraction
4.3.6 Localization of Abnormalities in MRI and CT Scanned Images
4.4 Results and Discussion 92 4.4.1 Metric Learning
4.4.2 Comparison of the Various Models for Image Retrieval
4.4.3 Precision vs. Recall Parameters Estimation for the CBIR
4.4.4 Convolutional Neural Networks–Based Landmark Localization
4.5 Conclusion
References
Part II: Applications of Deep Learning
5. Deep Learning for Clinical and Health Informatics
Amit Kumar Tyagi and Meghna Mannoj Nair
5.1 Introduction
5.1.1 Deep Learning Over Machine Learning
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
5.6.1 Types of Medical Imaging
5.6.2 Use and Benefits of Medical Imaging
5.7 Challenges Faced Toward Deep Learning Using Biomedical Imaging
5.7.1 Deep Learning in Healthcare: Limitations and Challenges
5.8 Open Research Issues and Future Research Directions Biomedical Imaging (Healthcare Informatics)
5.9 Conclusion
References
6. Biomedical Image Segmentation by Deep Learning Methods
K. Anita Davamani, C.R. Rene Robin, S. Amudha and L. Jani Anbarasi
6.1 Introduction
6.2 Overview of Deep Learning Algorithms
6.2.1 Deep Learning Classifier (DLC)
6.2.2 Deep Learning Architecture
6.3 Other Deep Learning Architecture
6.3.1 Restricted Boltzmann Machine (RBM)
6.3.2 Deep Learning Architecture Containing Autoencoders
6.3.3 Sparse Coding Deep Learning Architecture
6.3.4 Generative Adversarial Network (GAN)
6.3.5 Recurrent Neural Network (RNN)
6.4 Biomedical Image Segmentation
6.4.1 Clinical Images
6.4.2 X-Ray Imaging
6.4.3 Computed Tomography (CT)
6.4.4 Magnetic Resonance Imaging (MRI)
6.4.5 Ultrasound Imaging (US)
6.4.6 Optical Coherence Tomography (OCT)
6.5 Conclusion
References
7. Multi-Lingual Handwritten Character Recognition Using Deep Learning
Giriraj Parihar, Ratnavel Rajalakshmi and Bhuvana J.
7.1 Introduction
7.2 Related Works
7.3 Materials and Methods
7.4 Experiments and Results
7.4.1 Dataset Description
7.4.1.1 Handwritten Math Symbols
7.4.1.2 Bangla Handwritten Character Dataset
7.4.1.3 Devanagari Handwritten Character Dataset
7.4.2 Experimental Setup
7.4.3 Hype-Parameters
7.4.3.1 English Model
7.4.3.2 Hindi Model
7.4.3.3 Bangla Model
7.4.3.4 Math Symbol Model
7.4.3.5 Combined Model
7.4.4 Results and Discussion
7.4.4.1 Performance of Uni-Language Models
7.4.4.2 Uni-Language Model on English Dataset
7.4.4.3 Uni-Language Model on Hindi Dataset
7.4.4.4 Uni-Language Model on Bangla Dataset
7.4.4.5 Uni-Language Model on Math Symbol Dataset
7.4.4.6 Performance of Multi-Lingual Model on Combined Dataset
7.5 Conclusion
References
8. Disease Detection Through OpenCV
Neetu Faujdar and Aparna Sinha
8.1 Introduction
8.1.1 Image Processing
8.2 Problem Statement
8.2.1 Cataract
8.2.1.1 Causes
8.2.1.2 Types of Cataracts
8.2.1.3 Cataract Detection
8.2.1.4 Treatment
8.2.1.5 Prevention
8.2.1.6 Methodology
8.2.2 Eye Cancer
8.2.2.1 Symptoms
8.2.2.2 Causes of Retinoblastoma
8.2.2.3 Phases
8.2.2.4 Spreading of Cancer
8.2.2.5 Diagnosis
8.2.2.6 Treatment
8.2.2.7 Methodology
8.2.3 Skin Cancer (Melanoma)
8.2.3.1 Signs and Symptoms
8.2.3.2 Stages
8.2.3.3 Causes of Melanoma
8.2.3.4 Diagnosis
8.2.3.5 Treatment
8.2.3.6 Methodology
8.2.3.7 Asymmetry
8.2.3.8 Border
8.2.3.9 Color
8.2.3.10 Diameter Detection
8.2.3.11 Calculating TDS (Total Dermoscopy Score)
8.3 Conclusion
8.4 Summary
References
9. Computer-Aided Diagnosis of Liver Fibrosis in Hepatitis Patients Using Convolutional Neural Network
Aswathy S. U., Ajesh F., Shermin Shamsudheen and Jarin T.
9.1 Introduction
9.2 Overview of System
9.3 Methodology
9.3.1 Dataset
9.3.2 Pre-Processing
9.3.3 Feature Extraction
9.3.4 Feature Selection and Normalization
9.3.5 Classification Model
9.4 Performance and Analysis
9.5 Experimental Results
9.6 Conclusion and Future Scope
References
Part III: Future Deep Learning Models
10. Lung Cancer Prediction in Deep Learning Perspective
Nikita Banerjee and Subhalaxmi Das
10.1 Introduction
10.2 Machine Learning and Its Application
10.2.1 Machine Learning
10.2.2 Different Machine Learning Techniques
10.2.2.1 Decision Tree
10.2.2.2 Support Vector Machine
10.2.2.3 Random Forest
10.2.2.4 K-Means Clustering
10.3 Related Work
10.4 Why Deep Learning on Top of Machine Learning?
10.4.1 Deep Neural Network
10.4.2 Deep Belief Network
10.4.3 Convolutional Neural Network
10.5 How is Deep Learning Used for Prediction of Lungs Cancer?
10.5.1 Proposed Architecture
10.5.1.1 Pre-Processing Block
10.5.1.2 Segmentation
10.5.1.3 Classification
10.6 Conclusion
References
11. Lesion Detection and Classification for Breast Cancer Diagnosis Based on Deep CNNs from Digital Mammographic Data
Diksha Rajpal, Sumita Mishra and Anil Kumar
11.1 Introduction
11.2 Background
11.2.1 Methods of Diagnosis of Breast Cancer
11.2.2 Types of Breast Cancer
11.2.3 Breast Cancer Treatment Options
11.2.4 Limitations and Risks of Diagnosis and Treatment Options
11.2.4.1 Limitation of Diagnosis Methods
11.2.4.2 Limitations of Treatment Plans
11.2.5 Deep Learning Methods for Medical Image Analysis: Tumor Classification
11.3 Methods
11.3.1 Digital Repositories
11.3.1.1 DDSM Database
11.3.1.2 AMDI Database
11.3.1.3 IRMA Database
11.3.1.4 BreakHis Database
11.3.1.5 MIAS Database
11.3.2 Data Pre-Processing
11.3.2.1 Advantages of Pre-Processing Images
11.3.3 Convolutional Neural Networks (CNNs)
11.3.3.1 Architecture of CNN
11.3.4 Hyper-Parameters
11.3.4.1 Number of Hidden Layers
11.3.4.2 Dropout Rate
11.3.4.3 Activation Function
11.3.4.4 Learning Rate
11.3.4.5 Number of Epochs
11.3.4.6 Batch Size
11.3.5 Techniques to Improve CNN Performance
11.3.5.1 Hyper-Parameter Tuning
11.3.5.2 Augmenting Images
11.3.5.3 Managing Over-Fitting and Under-Fitting
11.4 Application of Deep CNN for Mammography
11.4.1 Lesion Detection and Localization
11.4.2 Lesion Classification
11.5 System Model and Results
11.5.1 System Model
11.5.2 System Flowchart
11.5.2.1 MIAS Database
11.5.2.2 Unannotated Images
11.5.3 Results
11.5.3.1 Distribution and Processing of Dataset
11.5.3.2 Training of the Model
11.5.3.3 Prediction of Unannotated Images
11.6 Research Challenges and Discussion on Future Directions
11.7 Conclusion
References
12. Health Prediction Analytics Using Deep Learning Methods and Applications
Sapna Jain, M. Afshar Alam, Nevine Makrim Labib and Eiad Yafi
12.1 Introduction
12.2 Background
12.3 Predictive Analytics
12.4 Deep Learning Predictive Analysis Applications
12.4.1 Deep Learning Application Model to Predict COVID-19 Infection
12.4.2 Deep Transfer Learning for Mitigating the COVID-19 Pandemic
12.4.3 Health Status Prediction for the Elderly Based on Machine Learning
12.4.4 Deep Learning in Machine Health Monitoring
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
Sophia S., Sridevi U.K., Boselin Prabhu S.R. and P. Thamaraiselvi
13.1 Introduction
13.2 Activities of Daily Living and Behavior Analysis
13.3 Intelligent Home Architecture
13.4 Methodology
13.4.1 Record the Behaviors Using Sensor Data
13.4.2 Classify Discrete Events and Relate the Events Using Data Analysis Algorithms
13.4.3 Construct Behavior Dictionaries for Flexible Event Intervals Using Deep Learning Concepts
13.4.4 Use the Dictionary in Modeling the Behavior Patterns Through Prediction Techniques
13.4.5 Detection of Deviations From Expected Behaviors Aiding the Automated Elderly Monitoring Based on Decision Support Algorithm Systems
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
V. Krishna Kumar, M.S. Geetha Devasena and G. Gopu
14.1 Introduction
14.2 Related Work
14.3 Existing System
14.4 Proposed System
14.4.1 Usage of 3D Slicer
14.5 Results and Discussion
14.6 Conclusion
References
Part IV: Deep Learning - Importance and Challenges for Other Sectors
15. Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities
Meenu Gupta, Akash Gupta and Gaganjot Kaur
15.1 Introduction
15.2 Related Work
15.3 Development of Personalized Medicine Using Deep Learning: A New Revolution in Healthcare Industry
15.3.1 Deep Feedforward Neural Network (DFF)
15.3.2 Convolutional Neural Network
15.3.3 Recurrent Neural Network (RNN)
15.3.4 Long/Short-Term Memory (LSTM)
15.3.5 Deep Belief Network (DBN)
15.3.6 Autoencoder (AE)
15.4 Deep Learning Applications in Precision Medicine
15.4.1 Discovery of Biomarker and Classification of Patient
15.4.2 Medical Imaging
15.5 Deep Learning for Medical Imaging
15.5.1 Medical Image Detection
15.5.1.1 Pathology Detection
15.5.1.2 Detection of Image Plane
15.5.1.3 Anatomical Landmark Localization
15.5.2 Medical Image Segmentation
15.5.2.1 Supervised Algorithms
15.5.2.2 Semi-Supervised Algorithms
15.5.3 Medical Image Enhancement
15.5.3.1 Two-Dimensional Super-Resolution Techniques
15.5.3.2 Three-Dimensional Super-Resolution Techniques
15.6 Drug Discovery and Development: A Promise Fulfilled by Deep Learning Technology
15.6.1 Prediction of Drug Properties
15.6.2 Prediction of Drug-Target Interaction
15.7 Application Areas of Deep Learning in Healthcare
15.7.1 Medical Chatbots
15.7.2 Smart Health Records
15.7.3 Cancer Diagnosis
15.8 Privacy Issues Arising With the Usage of Deep Learning in Healthcare
15.8.1 Private Data
15.8.2 Privacy Attacks
15.8.2.1 Evasion Attack
15.8.2.2 White-Box Attack
15.8.2.3 Black-Box Attack
15.8.2.4 Poisoning Attack
15.8.3 Privacy-Preserving Techniques
15.8.3.1 Differential Privacy With Deep Learning
15.8.3.2 Homomorphic Encryption (HE) on Deep Learning
15.8.3.3 Secure Multiparty Computation on Deep Learning
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
Ajeet K. Jain, PVRD Prasad Rao and K. Venkatesh Sharma
16.1 Introduction
16.1.1 Data Formats
16.1.1.1 Structured Data
16.1.1.2 Unstructured Data
16.1.1.3 Semi-Structured Data
16.1.2 Beginning With Learning Machines
16.1.2.1 Perception
16.1.2.2 Artificial Neural Network
16.1.2.3 Deep Networks and Learning
16.1.2.4 Model Selection, Over-Fitting, and Under-Fitting
16.2 Regularization in Machine Learning
16.2.1 Hamadard Conditions
16.2.2 Tikhonov Generalized Regularization
16.2.3 Ridge Regression
16.2.4 Lasso—L1 Regularization
16.2.5 Dropout as Regularization Feature
16.2.6 Augmenting Dataset
16.2.7 Early Stopping Criteria
16.3 Convexity Principles
16.3.1 Convex Sets
16.3.1.1 Affine Set and Convex Functions
16.3.1.2 Properties of Convex Functions
16.3.2 Optimization and Role of Optimizer in ML
16.3.2.1 Gradients-Descent Optimization Methods
16.3.2.2 Non-Convexity of Cost Functions
16.3.2.3 Basic Maths of SGD
16.3.2.4 Saddle Points
16.3.2.5 Gradient Pointing in the Wrong Direction
16.3.2.6 Momentum-Based Optimization
16.4 Conclusion and Discussion
References
17. Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges
S. Subasree and N. K. Sakthivel
17.1 Introduction
17.2 Machine Learning and Deep Learning Framework
17.2.1 Supervised Learning
17.2.2 Unsupervised Learning
17.2.3 Reinforcement Learning
17.2.4 Deep Learning
17.3 Challenges and Opportunities
17.3.1 Literature Review
17.4 Clinical Databases—Electronic Health Records
17.5 Data Analytics Models—Classifiers and Clusters
17.5.1 Criteria for Classification
17.5.1.1 Probabilistic Classifier
17.5.1.2 Support Vector Machines (SVMs)
17.5.1.3 K-Nearest Neighbors
17.5.2 Criteria for Clustering
17.5.2.1 K-Means Clustering
17.5.2.2 Mean Shift Clustering
17.6 Deep Learning Approaches and Association Predictions
17.6.1 G-HR: Gene Signature–Based HRF Cluster
17.6.1.1 G-HR Procedure
17.6.2 Deep Learning Approach and Association Predictions
17.6.2.1 Deep Learning Approach
17.6.2.2 Intelligent Human Disease-Gene Association Prediction Technique (IHDGAP)
17.6.2.3 Convolution Neural Network
17.6.2.4 Disease Semantic Similarity
17.6.2.5 Computation of Scoring Matrix
17.6.3 Identified Problem
17.6.4 Deep Learning–Based Human Diseases Pattern Prediction Technique for High-Dimensional Human Diseases Datasets (ECNN-HDPT)
17.6.5 Performance Analysis
17.7 Conclusion
17.8 Applications
References
18. Machine Learning and Deep Learning: Open Issues and Future Research Directions for the Next 10 Years
Akshara Pramod, Harsh Sankar Naicker and Amit Kumar Tyagi
18.1 Introduction
18.1.1 Comparison Among Data Mining, Machine Learning, and Deep Learning
18.1.2 Machine Learning
18.1.2.1 Importance of Machine Learning in Present Business Scenario
18.1.2.2 Applications of Machine Learning
18.1.2.3 Machine Learning Methods Used in Current Era
18.1.3 Deep Learning
18.1.3.1 Applications of Deep Learning
18.1.3.2 Deep Learning Techniques/Methods Used in Current Era
18.2 Evolution of Machine Learning and Deep Learning
18.3 The Forefront of Machine Learning Technology
18.3.1 Deep Learning
18.3.2 Reinforcement Learning
18.3.3 Transfer Learning
18.3.4 Adversarial Learning
18.3.5 Dual Learning
18.3.6 Distributed Machine Learning
18.3.7 Meta Learning
18.4 The Challenges Facing Machine Learning and Deep Learning
18.4.1 Explainable Machine Learning
18.4.2 Correlation and Causation
18.4.3 Machine Understands the Known and is Aware of the Unknown
18.4.4 People-Centric Machine Learning Evolution
18.4.5 Explainability: Stems From Practical Needs and Evolves Constantly
18.5 Possibilities With Machine Learning and Deep Learning
18.5.1 Possibilities With Machine Learning
18.5.1.1 Lightweight Machine Learning and Edge Computing
18.5.1.2 Quantum Machine Learning
18.5.1.3 Quantum Machine Learning Algorithms Based on Linear Algebra
18.5.1.4 Quantum Reinforcement Learning
18.5.1.5 Simple and Elegant Natural Laws
18.5.1.6 Improvisational Learning
18.5.1.7 Social Machine Learning
18.5.2 Possibilities With Deep Learning
18.5.2.1 Quantum Deep Learning
18.6 Potential Limitations of Machine Learning and Deep Learning
18.6.1 Machine Learning
18.6.2 Deep Learning
18.7 Conclusion
Acknowledgement
Contribution/Disclosure
References
Index

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