Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and Applications
- Length: 528 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2021-08-24
- ISBN-10: 1119785723
- ISBN-13: 9781119785729
- Sales Rank: #8975955 (See Top 100 Books)
This book discuss how deep learning can help healthcare images or text data in making useful decisions”. For that, the need of reliable deep learning models like Neural networks, Convolutional neural network, Backpropagation, Recurrent neural network is increasing in medical image processing, i.e., in Colorization of Black and white images of X-Ray, automatic machine translation, object classification in photographs / images (CT-SCAN), character or useful generation (ECG), image caption generation, etc. Hence, Reliable Deep Learning methods for perception or producing belter results are highly effective for e-healthcare applications, which is the challenge of today. For that, this book provides some reliable deep leaning or deep neural networks models for healthcare applications via receiving chapters from around the world. In summary, this book will cover introduction, requirement, importance, issues and challenges, etc., faced in available current deep learning models (also include innovative deep learning algorithms/ models for curing disease in Medicare) and provide opportunities for several research communities with including several research gaps in deep learning models (for healthcare applications).
Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface Part 1: DEEP LEARNING AND ITS MODELS 1. CNN: A Review of Models, Application of IVD Segmentation 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 2.1 Introduction 2.2 Related Work 2.3 Artificial Intelligence Perspective 2.3.1 Keyword Query Suggestion 2.3.2 User Preference From Log 2.3.3 Location-Aware Keyword Query Suggestion 2.3.4 Enhancement With AI Perspective 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 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 3.5 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 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 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 2: APPLICATIONS OF DEEP LEARNING 5. Deep Learning for Clinical and Health Informatics 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 Uses and Benefits of Medical Imaging 5.7 Challenges Faced Toward Deep Learning Using in Biomedical Imaging 5.7.1 Deep Learning in Healthcare: Limitations and Challenges 5.8 Open Research Issues and Future Research Directions in Biomedical Imaging (Healthcare Informatics) 5.9 Conclusion References 6. Biomedical Image Segmentation by Deep Learning Methods 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 7.1 Introduction 7.2 Related Works 7.3 Materials and Methods 7.4 Experiments and Results 7.4.1 Dataset Description 7.4.2 Experimental Setup 7.4.3 Hype-Parameters 7.4.4 Results and Discussion 7.5 Conclusion References 8. Disease Detection Platform Using Image Processing Through OpenCV 8.1 Introduction 8.1.1 Image Processing 8.2 Problem Statement 8.2.1 Cataract 8.2.2 Eye Cancer 8.2.3 Skin Cancer (Melanoma) 8.3 Conclusion 8.4 Summary References 9. Computer-Aided Diagnosis of Liver Fibrosis in Hepatitis Patients Using Convolutional Neural Network 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 3: FUTURE DEEP LEARNING MODELS 10. Lung Cancer Prediction in Deep Learning Perspective 10.1 Introduction 10.2 Machine Learning and Its Application 10.2.1 Machine Learning 10.2.2 Different Machine Learning Techniques 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 Convolutio nal Neural Network 10.5 How is Deep Learning Used for Prediction of Lungs Cancer? 10.5.1 Proposed Architecture 10.6 Conclusion References 11. Lesion Detection and Classification for Breast Cancer Diagnosis Based on Deep CNNs from Digital Mammographic Data 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 11.2.5 Deep Learning Methods for Medical Image Analysis: Tumo r Classification 11.3 Methods 11.3.1 Digital Repositories 11.3.2 Data Pre-Processing 11.3.3 Convolutional Neural Networks (CNNs) 11.3.4 Hyper-Parameters 11.3.5 Techniques to Improve CNN Performance 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 11.5.3 Results 11.6 Research Challenges and Discussion on Future Directions 11.7 Conclusion References 12. Health Prediction Analytics Using Deep Learning Methods and Applications 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 Predict 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 References 14. Early Diagnosis Tool for Alzheimer’s Disease Using 3D Slicer 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 4: DEEP LEARNING - IMPORTANCE AND CHALLENGES FOR OTHER SECTORS 15. Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities 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 15.5.2 Medical Image Segmentation 15.5.3 Medical Image Enhancement 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 15.8.3 Privacy-Preserving Techniques 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 16.1 Introduction 16.1.1 Data Formats 16.1.2 Beginning With Learning Machines 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 16.3.2 Optimization and Role of Optimizer in ML 16.4 Conclusion and Discussion References 17. Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges 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 17.5.2 Criteria for Clustering 17.6 Deep Learning Approaches and Association Predictions 17.6.1 G-HR: Gene Signature–Based HRF Cluster 17.6.2 Deep Learning Approach and Association Predictions 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 18.1 Introduction 18.1.1 Comparison Among Data Mining, Machine Learning, and Deep Learning 18.1.2 Machine Learning 18.1.3 Deep Learning 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 18.5.2 Possibilities With 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 EULA
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