{"product_id":"computational-analysis-and-deep-learning-for-medical-care-9781119785729","title":"Computational Analysis and Deep Learning for","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eWe 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 ne\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Deep Learning and Its Models 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 CNN: A Review of Models, Application of IVD Segmentation 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLeena Silvoster M. and R. Mathusoothana S. Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.2 Various CNN Models 4\u003c\/p\u003e \u003cp\u003e1.2.1 LeNet-5 4\u003c\/p\u003e \u003cp\u003e1.2.2 AlexNet 7\u003c\/p\u003e \u003cp\u003e1.2.3 ZFNet 8\u003c\/p\u003e \u003cp\u003e1.2.4 VGGNet 10\u003c\/p\u003e \u003cp\u003e1.2.5 GoogLeNet 12\u003c\/p\u003e \u003cp\u003e1.2.6 ResNet 16\u003c\/p\u003e \u003cp\u003e1.2.7 ResNeXt 21\u003c\/p\u003e \u003cp\u003e1.2.8 SE-ResNet 24\u003c\/p\u003e \u003cp\u003e1.2.9 DenseNet 24\u003c\/p\u003e \u003cp\u003e1.2.10 MobileNets 25\u003c\/p\u003e \u003cp\u003e1.3 Application of CNN to IVD Detection 26\u003c\/p\u003e \u003cp\u003e1.4 Comparison With State-of-the-Art Segmentation Approaches for Spine T2W Images 28\u003c\/p\u003e \u003cp\u003e1.5 Conclusion 28\u003c\/p\u003e \u003cp\u003eReferences 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Location-Aware Keyword Query Suggestion Techniques With Artificial Intelligence Perspective 35\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eR. Ravinder Reddy, C. Vaishnavi, Ch. Mamatha and S. Ananthakumaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 36\u003c\/p\u003e \u003cp\u003e2.2 Related Work 39\u003c\/p\u003e \u003cp\u003e2.3 Artificial Intelligence Perspective 41\u003c\/p\u003e \u003cp\u003e2.3.1 Keyword Query Suggestion 42\u003c\/p\u003e \u003cp\u003e2.3.1.1 Random Walk–Based Approaches 42\u003c\/p\u003e \u003cp\u003e2.3.1.2 Cluster-Based Approaches 42\u003c\/p\u003e \u003cp\u003e2.3.1.3 Learning to Rank Approaches 43\u003c\/p\u003e \u003cp\u003e2.3.2 User Preference From Log 43\u003c\/p\u003e \u003cp\u003e2.3.3 Location-Aware Keyword Query Suggestion 44\u003c\/p\u003e \u003cp\u003e2.3.4 Enhancement With AI Perspective 44\u003c\/p\u003e \u003cp\u003e2.3.4.1 Case Study 45\u003c\/p\u003e \u003cp\u003e2.4 Architecture 46\u003c\/p\u003e \u003cp\u003e2.4.1 Distance Measures 47\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 49\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Identification of a Suitable Transfer Learning Architecture for Classification: A Case Study with Liver Tumors 53\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eB. Lakshmi Priya, K. Jayanthi, Biju Pottakkat and G. Ramkumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 54\u003c\/p\u003e \u003cp\u003e3.2 Related Works 56\u003c\/p\u003e \u003cp\u003e3.3 Convolutional Neural Networks 58\u003c\/p\u003e \u003cp\u003e3.3.1 Feature Learning in CNNs 59\u003c\/p\u003e \u003cp\u003e3.3.2 Classification in CNNs 60\u003c\/p\u003e \u003cp\u003e3.4 Transfer Learning 61\u003c\/p\u003e \u003cp\u003e3.4.1 AlexNet 61\u003c\/p\u003e \u003cp\u003e3.4.2 GoogLeNet 62\u003c\/p\u003e \u003cp\u003e3.4.3 Residual Networks 63\u003c\/p\u003e \u003cp\u003e3.4.3.1 ResNet-18 65\u003c\/p\u003e \u003cp\u003e3.4.3.2 ResNet-50 65\u003c\/p\u003e \u003cp\u003e3.5 System Model 66\u003c\/p\u003e \u003cp\u003e3.6 Results and Discussions 67\u003c\/p\u003e \u003cp\u003e3.6.1 Dataset 67\u003c\/p\u003e \u003cp\u003e3.6.2 Assessment of Transfer Learning Architectures 67\u003c\/p\u003e \u003cp\u003e3.7 Conclusion 73\u003c\/p\u003e \u003cp\u003eReferences 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Optimization and Deep Learning-Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images 79\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSuresh Kumar K., Sundaresan S., Nishanth R. and Ananth Kumar T.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 80\u003c\/p\u003e \u003cp\u003e4.2 Related Works 82\u003c\/p\u003e \u003cp\u003e4.3 Proposed Method 85\u003c\/p\u003e \u003cp\u003e4.3.1 Input Dataset 86\u003c\/p\u003e \u003cp\u003e4.3.2 Pre-Processing 86\u003c\/p\u003e \u003cp\u003e4.3.3 Combination of DCNN and CFML 86\u003c\/p\u003e \u003cp\u003e4.3.4 Fine Tuning and Optimization 88\u003c\/p\u003e \u003cp\u003e4.3.5 Feature Extraction 89\u003c\/p\u003e \u003cp\u003e4.3.6 Localization of Abnormalities in MRI and CT Scanned Images 90\u003c\/p\u003e \u003cp\u003e4.4 Results and Discussion 92\u003c\/p\u003e \u003cp\u003e4.4.1 Metric Learning 92\u003c\/p\u003e \u003cp\u003e4.4.2 Comparison of the Various Models for Image Retrieval 92\u003c\/p\u003e \u003cp\u003e4.4.3 Precision vs. Recall Parameters Estimation for the CBIR 93\u003c\/p\u003e \u003cp\u003e4.4.4 Convolutional Neural Networks–Based Landmark Localization 96\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 104\u003c\/p\u003e \u003cp\u003eReferences 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Applications of Deep Learning 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Deep Learning for Clinical and Health Informatics 109\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAmit Kumar Tyagi and Meghna Mannoj Nair\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 110\u003c\/p\u003e \u003cp\u003e5.1.1 Deep Learning Over Machine Learning 111\u003c\/p\u003e \u003cp\u003e5.2 Related Work 113\u003c\/p\u003e \u003cp\u003e5.3 Motivation 115\u003c\/p\u003e \u003cp\u003e5.4 Scope of the Work in Past, Present, and Future 115\u003c\/p\u003e \u003cp\u003e5.5 Deep Learning Tools, Methods Available for Clinical, and Health Informatics 117\u003c\/p\u003e \u003cp\u003e5.6 Deep Learning: Not-So-Near Future in Biomedical Imaging 119\u003c\/p\u003e \u003cp\u003e5.6.1 Types of Medical Imaging 119\u003c\/p\u003e \u003cp\u003e5.6.2 Use and Benefits of Medical Imaging 120\u003c\/p\u003e \u003cp\u003e5.7 Challenges Faced Toward Deep Learning Using Biomedical Imaging 121\u003c\/p\u003e \u003cp\u003e5.7.1 Deep Learning in Healthcare: Limitations and Challenges 122\u003c\/p\u003e \u003cp\u003e5.8 Open Research Issues and Future Research Directions Biomedical Imaging (Healthcare Informatics) 124\u003c\/p\u003e \u003cp\u003e5.9 Conclusion 127\u003c\/p\u003e \u003cp\u003eReferences 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Biomedical Image Segmentation by Deep Learning Methods 131\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eK. Anita Davamani, C.R. Rene Robin, S. Amudha and L. Jani Anbarasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 132\u003c\/p\u003e \u003cp\u003e6.2 Overview of Deep Learning Algorithms 135\u003c\/p\u003e \u003cp\u003e6.2.1 Deep Learning Classifier (DLC) 136\u003c\/p\u003e \u003cp\u003e6.2.2 Deep Learning Architecture 137\u003c\/p\u003e \u003cp\u003e6.3 Other Deep Learning Architecture 139\u003c\/p\u003e \u003cp\u003e6.3.1 Restricted Boltzmann Machine (RBM) 139\u003c\/p\u003e \u003cp\u003e6.3.2 Deep Learning Architecture Containing Autoencoders 140\u003c\/p\u003e \u003cp\u003e6.3.3 Sparse Coding Deep Learning Architecture 141\u003c\/p\u003e \u003cp\u003e6.3.4 Generative Adversarial Network (GAN) 141\u003c\/p\u003e \u003cp\u003e6.3.5 Recurrent Neural Network (RNN) 141\u003c\/p\u003e \u003cp\u003e6.4 Biomedical Image Segmentation 145\u003c\/p\u003e \u003cp\u003e6.4.1 Clinical Images 146\u003c\/p\u003e \u003cp\u003e6.4.2 X-Ray Imaging 146\u003c\/p\u003e \u003cp\u003e6.4.3 Computed Tomography (CT) 147\u003c\/p\u003e \u003cp\u003e6.4.4 Magnetic Resonance Imaging (MRI) 147\u003c\/p\u003e \u003cp\u003e6.4.5 Ultrasound Imaging (US) 148\u003c\/p\u003e \u003cp\u003e6.4.6 Optical Coherence Tomography (OCT) 148\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 149\u003c\/p\u003e \u003cp\u003eReferences 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Multi-Lingual Handwritten Character Recognition Using Deep Learning 155\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGiriraj Parihar, Ratnavel Rajalakshmi and Bhuvana J.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 156\u003c\/p\u003e \u003cp\u003e7.2 Related Works 157\u003c\/p\u003e \u003cp\u003e7.3 Materials and Methods 160\u003c\/p\u003e \u003cp\u003e7.4 Experiments and Results 161\u003c\/p\u003e \u003cp\u003e7.4.1 Dataset Description 162\u003c\/p\u003e \u003cp\u003e7.4.1.1 Handwritten Math Symbols 162\u003c\/p\u003e \u003cp\u003e7.4.1.2 Bangla Handwritten Character Dataset 162\u003c\/p\u003e \u003cp\u003e7.4.1.3 Devanagari Handwritten Character Dataset 162\u003c\/p\u003e \u003cp\u003e7.4.2 Experimental Setup 162\u003c\/p\u003e \u003cp\u003e7.4.3 Hype-Parameters 164\u003c\/p\u003e \u003cp\u003e7.4.3.1 English Model 164\u003c\/p\u003e \u003cp\u003e7.4.3.2 Hindi Model 165\u003c\/p\u003e \u003cp\u003e7.4.3.3 Bangla Model 165\u003c\/p\u003e \u003cp\u003e7.4.3.4 Math Symbol Model 165\u003c\/p\u003e \u003cp\u003e7.4.3.5 Combined Model 166\u003c\/p\u003e \u003cp\u003e7.4.4 Results and Discussion 167\u003c\/p\u003e \u003cp\u003e7.4.4.1 Performance of Uni-Language Models 167\u003c\/p\u003e \u003cp\u003e7.4.4.2 Uni-Language Model on English Dataset 168\u003c\/p\u003e \u003cp\u003e7.4.4.3 Uni-Language Model on Hindi Dataset 168\u003c\/p\u003e \u003cp\u003e7.4.4.4 Uni-Language Model on Bangla Dataset 169\u003c\/p\u003e \u003cp\u003e7.4.4.5 Uni-Language Model on Math Symbol Dataset 169\u003c\/p\u003e \u003cp\u003e7.4.4.6 Performance of Multi-Lingual Model on Combined Dataset 171\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 177\u003c\/p\u003e \u003cp\u003eReferences 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Disease Detection Platform Using Image Processing Through OpenCV 181\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNeetu Faujdar and Aparna Sinha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 182\u003c\/p\u003e \u003cp\u003e8.1.1 Image Processing 183\u003c\/p\u003e \u003cp\u003e8.2 Problem Statement 183\u003c\/p\u003e \u003cp\u003e8.2.1 Cataract 183\u003c\/p\u003e \u003cp\u003e8.2.1.1 Causes 184\u003c\/p\u003e \u003cp\u003e8.2.1.2 Types of Cataracts 184\u003c\/p\u003e \u003cp\u003e8.2.1.3 Cataract Detection 185\u003c\/p\u003e \u003cp\u003e8.2.1.4 Treatment 186\u003c\/p\u003e \u003cp\u003e8.2.1.5 Prevention 186\u003c\/p\u003e \u003cp\u003e8.2.1.6 Methodology 186\u003c\/p\u003e \u003cp\u003e8.2.2 Eye Cancer 192\u003c\/p\u003e \u003cp\u003e8.2.2.1 Symptoms 194\u003c\/p\u003e \u003cp\u003e8.2.2.2 Causes of Retinoblastoma 194\u003c\/p\u003e \u003cp\u003e8.2.2.3 Phases 195\u003c\/p\u003e \u003cp\u003e8.2.2.4 Spreading of Cancer 196\u003c\/p\u003e \u003cp\u003e8.2.2.5 Diagnosis 196\u003c\/p\u003e \u003cp\u003e8.2.2.6 Treatment 197\u003c\/p\u003e \u003cp\u003e8.2.2.7 Methodology 199\u003c\/p\u003e \u003cp\u003e8.2.3 Skin Cancer (Melanoma) 202\u003c\/p\u003e \u003cp\u003e8.2.3.1 Signs and Symptoms 203\u003c\/p\u003e \u003cp\u003e8.2.3.2 Stages 203\u003c\/p\u003e \u003cp\u003e8.2.3.3 Causes of Melanoma 204\u003c\/p\u003e \u003cp\u003e8.2.3.4 Diagnosis 204\u003c\/p\u003e \u003cp\u003e8.2.3.5 Treatment 205\u003c\/p\u003e \u003cp\u003e8.2.3.6 Methodology 206\u003c\/p\u003e \u003cp\u003e8.2.3.7 Asymmetry 207\u003c\/p\u003e \u003cp\u003e8.2.3.8 Border 208\u003c\/p\u003e \u003cp\u003e8.2.3.9 Color 208\u003c\/p\u003e \u003cp\u003e8.2.3.10 Diameter Detection 209\u003c\/p\u003e \u003cp\u003e8.2.3.11 Calculating TDS (Total Dermoscopy Score) 210\u003c\/p\u003e \u003cp\u003e8.3 Conclusion 210\u003c\/p\u003e \u003cp\u003e8.4 Summary 212\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Computer-Aided Diagnosis of Liver Fibrosis in Hepatitis Patients Using Convolutional Neural Network 217\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAswathy S. U., Ajesh F., Shermin Shamsudheen and Jarin T.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 218\u003c\/p\u003e \u003cp\u003e9.2 Overview of System 219\u003c\/p\u003e \u003cp\u003e9.3 Methodology 219\u003c\/p\u003e \u003cp\u003e9.3.1 Dataset 220\u003c\/p\u003e \u003cp\u003e9.3.2 Pre-Processing 221\u003c\/p\u003e \u003cp\u003e9.3.3 Feature Extraction 221\u003c\/p\u003e \u003cp\u003e9.3.4 Feature Selection and Normalization 223\u003c\/p\u003e \u003cp\u003e9.3.5 Classification Model 225\u003c\/p\u003e \u003cp\u003e9.4 Performance and Analysis 227\u003c\/p\u003e \u003cp\u003e9.5 Experimental Results 232\u003c\/p\u003e \u003cp\u003e9.6 Conclusion and Future Scope 232\u003c\/p\u003e \u003cp\u003eReferences 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Future Deep Learning Models 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Lung Cancer Prediction in Deep Learning Perspective 239\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNikita Banerjee and Subhalaxmi Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 239\u003c\/p\u003e \u003cp\u003e10.2 Machine Learning and Its Application 240\u003c\/p\u003e \u003cp\u003e10.2.1 Machine Learning 240\u003c\/p\u003e \u003cp\u003e10.2.2 Different Machine Learning Techniques 241\u003c\/p\u003e \u003cp\u003e10.2.2.1 Decision Tree 242\u003c\/p\u003e \u003cp\u003e10.2.2.2 Support Vector Machine 242\u003c\/p\u003e \u003cp\u003e10.2.2.3 Random Forest 242\u003c\/p\u003e \u003cp\u003e10.2.2.4 K-Means Clustering 242\u003c\/p\u003e \u003cp\u003e10.3 Related Work 243\u003c\/p\u003e \u003cp\u003e10.4 Why Deep Learning on Top of Machine Learning? 245\u003c\/p\u003e \u003cp\u003e10.4.1 Deep Neural Network 246\u003c\/p\u003e \u003cp\u003e10.4.2 Deep Belief Network 247\u003c\/p\u003e \u003cp\u003e10.4.3 Convolutional Neural Network 247\u003c\/p\u003e \u003cp\u003e10.5 How is Deep Learning Used for Prediction of Lungs Cancer? 248\u003c\/p\u003e \u003cp\u003e10.5.1 Proposed Architecture 248\u003c\/p\u003e \u003cp\u003e10.5.1.1 Pre-Processing Block 250\u003c\/p\u003e \u003cp\u003e10.5.1.2 Segmentation 250\u003c\/p\u003e \u003cp\u003e10.5.1.3 Classification 252\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 253\u003c\/p\u003e \u003cp\u003eReferences 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Lesion Detection and Classification for Breast Cancer Diagnosis Based on Deep CNNs from Digital Mammographic Data 257\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDiksha Rajpal, Sumita Mishra and Anil Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 257\u003c\/p\u003e \u003cp\u003e11.2 Background 258\u003c\/p\u003e \u003cp\u003e11.2.1 Methods of Diagnosis of Breast Cancer 258\u003c\/p\u003e \u003cp\u003e11.2.2 Types of Breast Cancer 260\u003c\/p\u003e \u003cp\u003e11.2.3 Breast Cancer Treatment Options 261\u003c\/p\u003e \u003cp\u003e11.2.4 Limitations and Risks of Diagnosis and Treatment Options 262\u003c\/p\u003e \u003cp\u003e11.2.4.1 Limitation of Diagnosis Methods 262\u003c\/p\u003e \u003cp\u003e11.2.4.2 Limitations of Treatment Plans 263\u003c\/p\u003e \u003cp\u003e11.2.5 Deep Learning Methods for Medical Image Analysis: Tumor Classification 263\u003c\/p\u003e \u003cp\u003e11.3 Methods 265\u003c\/p\u003e \u003cp\u003e11.3.1 Digital Repositories 265\u003c\/p\u003e \u003cp\u003e11.3.1.1 DDSM Database 265\u003c\/p\u003e \u003cp\u003e11.3.1.2 AMDI Database 265\u003c\/p\u003e \u003cp\u003e11.3.1.3 IRMA Database 265\u003c\/p\u003e \u003cp\u003e11.3.1.4 BreakHis Database 265\u003c\/p\u003e \u003cp\u003e11.3.1.5 MIAS Database 266\u003c\/p\u003e \u003cp\u003e11.3.2 Data Pre-Processing 266\u003c\/p\u003e \u003cp\u003e11.3.2.1 Advantages of Pre-Processing Images 267\u003c\/p\u003e \u003cp\u003e11.3.3 Convolutional Neural Networks (CNNs) 268\u003c\/p\u003e \u003cp\u003e11.3.3.1 Architecture of CNN 269\u003c\/p\u003e \u003cp\u003e11.3.4 Hyper-Parameters 272\u003c\/p\u003e \u003cp\u003e11.3.4.1 Number of Hidden Layers 273\u003c\/p\u003e \u003cp\u003e11.3.4.2 Dropout Rate 273\u003c\/p\u003e \u003cp\u003e11.3.4.3 Activation Function 273\u003c\/p\u003e \u003cp\u003e11.3.4.4 Learning Rate 274\u003c\/p\u003e \u003cp\u003e11.3.4.5 Number of Epochs 274\u003c\/p\u003e \u003cp\u003e11.3.4.6 Batch Size 274\u003c\/p\u003e \u003cp\u003e11.3.5 Techniques to Improve CNN Performance 274\u003c\/p\u003e \u003cp\u003e11.3.5.1 Hyper-Parameter Tuning 274\u003c\/p\u003e \u003cp\u003e11.3.5.2 Augmenting Images 274\u003c\/p\u003e \u003cp\u003e11.3.5.3 Managing Over-Fitting and Under-Fitting 275\u003c\/p\u003e \u003cp\u003e11.4 Application of Deep CNN for Mammography 275\u003c\/p\u003e \u003cp\u003e11.4.1 Lesion Detection and Localization 275\u003c\/p\u003e \u003cp\u003e11.4.2 Lesion Classification 279\u003c\/p\u003e \u003cp\u003e11.5 System Model and Results 280\u003c\/p\u003e \u003cp\u003e11.5.1 System Model 280\u003c\/p\u003e \u003cp\u003e11.5.2 System Flowchart 281\u003c\/p\u003e \u003cp\u003e11.5.2.1 MIAS Database 281\u003c\/p\u003e \u003cp\u003e11.5.2.2 Unannotated Images 281\u003c\/p\u003e \u003cp\u003e11.5.3 Results 282\u003c\/p\u003e \u003cp\u003e11.5.3.1 Distribution and Processing of Dataset 282\u003c\/p\u003e \u003cp\u003e11.5.3.2 Training of the Model 283\u003c\/p\u003e \u003cp\u003e11.5.3.3 Prediction of Unannotated Images 286\u003c\/p\u003e \u003cp\u003e11.6 Research Challenges and Discussion on Future Directions 286\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 288\u003c\/p\u003e \u003cp\u003eReferences 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Health Prediction Analytics Using Deep Learning Methods and Applications 293\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSapna Jain, M. Afshar Alam, Nevine Makrim Labib and Eiad Yafi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 294\u003c\/p\u003e \u003cp\u003e12.2 Background 298\u003c\/p\u003e \u003cp\u003e12.3 Predictive Analytics 299\u003c\/p\u003e \u003cp\u003e12.4 Deep Learning Predictive Analysis Applications 305\u003c\/p\u003e \u003cp\u003e12.4.1 Deep Learning Application Model to Predict COVID-19 Infection 305\u003c\/p\u003e \u003cp\u003e12.4.2 Deep Transfer Learning for Mitigating the COVID-19 Pandemic 308\u003c\/p\u003e \u003cp\u003e12.4.3 Health Status Prediction for the Elderly Based on Machine Learning 309\u003c\/p\u003e \u003cp\u003e12.4.4 Deep Learning in Machine Health Monitoring 311\u003c\/p\u003e \u003cp\u003e12.5 Discussion 319\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 320\u003c\/p\u003e \u003cp\u003eReferences 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Ambient-Assisted Living of Disabled Elderly in an Intelligent Home Using Behavior Prediction—A Reliable Deep Learning Prediction System 329\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSophia S., Sridevi U.K., Boselin Prabhu S.R. and P. Thamaraiselvi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 330\u003c\/p\u003e \u003cp\u003e13.2 Activities of Daily Living and Behavior Analysis 331\u003c\/p\u003e \u003cp\u003e13.3 Intelligent Home Architecture 333\u003c\/p\u003e \u003cp\u003e13.4 Methodology 335\u003c\/p\u003e \u003cp\u003e13.4.1 Record the Behaviors Using Sensor Data 335\u003c\/p\u003e \u003cp\u003e13.4.2 Classify Discrete Events and Relate the Events Using Data Analysis Algorithms 335\u003c\/p\u003e \u003cp\u003e13.4.3 Construct Behavior Dictionaries for Flexible Event Intervals Using Deep Learning Concepts 335\u003c\/p\u003e \u003cp\u003e13.4.4 Use the Dictionary in Modeling the Behavior Patterns Through Prediction Techniques 336\u003c\/p\u003e \u003cp\u003e13.4.5 Detection of Deviations From Expected Behaviors Aiding the Automated Elderly Monitoring Based on Decision Support Algorithm Systems 336\u003c\/p\u003e \u003cp\u003e13.5 Senior Analytics Care Model 337\u003c\/p\u003e \u003cp\u003e13.6 Results and Discussions 338\u003c\/p\u003e \u003cp\u003e13.7 Conclusion 341\u003c\/p\u003e \u003cp\u003eNomenclature 341\u003c\/p\u003e \u003cp\u003eReferences 342\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Early Diagnosis Tool for Alzheimer’s Disease Using 3D Slicer 343\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eV. Krishna Kumar, M.S. Geetha Devasena and G. Gopu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 344\u003c\/p\u003e \u003cp\u003e14.2 Related Work 345\u003c\/p\u003e \u003cp\u003e14.3 Existing System 347\u003c\/p\u003e \u003cp\u003e14.4 Proposed System 347\u003c\/p\u003e \u003cp\u003e14.4.1 Usage of 3D Slicer 350\u003c\/p\u003e \u003cp\u003e14.5 Results and Discussion 353\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 356\u003c\/p\u003e \u003cp\u003eReferences 356\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Deep Learning – Importance and Challenges for Other Sectors 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities 363\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMeenu Gupta, Akash Gupta and Gaganjot Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 364\u003c\/p\u003e \u003cp\u003e15.2 Related Work 365\u003c\/p\u003e \u003cp\u003e15.3 Development of Personalized Medicine Using Deep Learning: A New Revolution in Healthcare Industry 367\u003c\/p\u003e \u003cp\u003e15.3.1 Deep Feedforward Neural Network (DFF) 367\u003c\/p\u003e \u003cp\u003e15.3.2 Convolutional Neural Network 367\u003c\/p\u003e \u003cp\u003e15.3.3 Recurrent Neural Network (RNN) 369\u003c\/p\u003e \u003cp\u003e15.3.4 Long\/Short-Term Memory (LSTM) 369\u003c\/p\u003e \u003cp\u003e15.3.5 Deep Belief Network (DBN) 370\u003c\/p\u003e \u003cp\u003e15.3.6 Autoencoder (AE) 370\u003c\/p\u003e \u003cp\u003e15.4 Deep Learning Applications in Precision Medicine 370\u003c\/p\u003e \u003cp\u003e15.4.1 Discovery of Biomarker and Classification of Patient 370\u003c\/p\u003e \u003cp\u003e15.4.2 Medical Imaging 371\u003c\/p\u003e \u003cp\u003e15.5 Deep Learning for Medical Imaging 372\u003c\/p\u003e \u003cp\u003e15.5.1 Medical Image Detection 372\u003c\/p\u003e \u003cp\u003e15.5.1.1 Pathology Detection 372\u003c\/p\u003e \u003cp\u003e15.5.1.2 Detection of Image Plane 373\u003c\/p\u003e \u003cp\u003e15.5.1.3 Anatomical Landmark Localization 373\u003c\/p\u003e \u003cp\u003e15.5.2 Medical Image Segmentation 373\u003c\/p\u003e \u003cp\u003e15.5.2.1 Supervised Algorithms 374\u003c\/p\u003e \u003cp\u003e15.5.2.2 Semi-Supervised Algorithms 374\u003c\/p\u003e \u003cp\u003e15.5.3 Medical Image Enhancement 375\u003c\/p\u003e \u003cp\u003e15.5.3.1 Two-Dimensional Super-Resolution Techniques 375\u003c\/p\u003e \u003cp\u003e15.5.3.2 Three-Dimensional Super-Resolution Techniques 375\u003c\/p\u003e \u003cp\u003e15.6 Drug Discovery and Development: A Promise Fulfilled by Deep Learning Technology 375\u003c\/p\u003e \u003cp\u003e15.6.1 Prediction of Drug Properties 376\u003c\/p\u003e \u003cp\u003e15.6.2 Prediction of Drug-Target Interaction 377\u003c\/p\u003e \u003cp\u003e15.7 Application Areas of Deep Learning in Healthcare 377\u003c\/p\u003e \u003cp\u003e15.7.1 Medical Chatbots 377\u003c\/p\u003e \u003cp\u003e15.7.2 Smart Health Records 377\u003c\/p\u003e \u003cp\u003e15.7.3 Cancer Diagnosis 378\u003c\/p\u003e \u003cp\u003e15.8 Privacy Issues Arising With the Usage of Deep Learning in Healthcare 379\u003c\/p\u003e \u003cp\u003e15.8.1 Private Data 379\u003c\/p\u003e \u003cp\u003e15.8.2 Privacy Attacks 380\u003c\/p\u003e \u003cp\u003e15.8.2.1 Evasion Attack 380\u003c\/p\u003e \u003cp\u003e15.8.2.2 White-Box Attack 380\u003c\/p\u003e \u003cp\u003e15.8.2.3 Black-Box Attack 380\u003c\/p\u003e \u003cp\u003e15.8.2.4 Poisoning Attack 381\u003c\/p\u003e \u003cp\u003e15.8.3 Privacy-Preserving Techniques 381\u003c\/p\u003e \u003cp\u003e15.8.3.1 Differential Privacy With Deep Learning 381\u003c\/p\u003e \u003cp\u003e15.8.3.2 Homomorphic Encryption (HE) on Deep Learning 382\u003c\/p\u003e \u003cp\u003e15.8.3.3 Secure Multiparty Computation on Deep Learning 383\u003c\/p\u003e \u003cp\u003e15.9 Challenges and Opportunities in Healthcare Using Deep Learning 383\u003c\/p\u003e \u003cp\u003e15.10 Conclusion and Future Scope 386\u003c\/p\u003e \u003cp\u003eReferences 387\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 A Perspective Analysis of Regularization and Optimization Techniques in Machine Learning 393\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAjeet K. Jain, PVRD Prasad Rao and K. Venkatesh Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 394\u003c\/p\u003e \u003cp\u003e16.1.1 Data Formats 395\u003c\/p\u003e \u003cp\u003e16.1.1.1 Structured Data 395\u003c\/p\u003e \u003cp\u003e16.1.1.2 Unstructured Data 396\u003c\/p\u003e \u003cp\u003e16.1.1.3 Semi-Structured Data 396\u003c\/p\u003e \u003cp\u003e16.1.2 Beginning With Learning Machines 397\u003c\/p\u003e \u003cp\u003e16.1.2.1 Perception 397\u003c\/p\u003e \u003cp\u003e16.1.2.2 Artificial Neural Network 398\u003c\/p\u003e \u003cp\u003e16.1.2.3 Deep Networks and Learning 399\u003c\/p\u003e \u003cp\u003e16.1.2.4 Model Selection, Over-Fitting, and Under-Fitting 400\u003c\/p\u003e \u003cp\u003e16.2 Regularization in Machine Learning 402\u003c\/p\u003e \u003cp\u003e16.2.1 Hamadard Conditions 403\u003c\/p\u003e \u003cp\u003e16.2.2 Tikhonov Generalized Regularization 404\u003c\/p\u003e \u003cp\u003e16.2.3 Ridge Regression 406\u003c\/p\u003e \u003cp\u003e16.2.4 Lasso—L1 Regularization 406\u003c\/p\u003e \u003cp\u003e16.2.5 Dropout as Regularization Feature 407\u003c\/p\u003e \u003cp\u003e16.2.6 Augmenting Dataset 408\u003c\/p\u003e \u003cp\u003e16.2.7 Early Stopping Criteria 408\u003c\/p\u003e \u003cp\u003e16.3 Convexity Principles 409\u003c\/p\u003e \u003cp\u003e16.3.1 Convex Sets 410\u003c\/p\u003e \u003cp\u003e16.3.1.1 Affine Set and Convex Functions 411\u003c\/p\u003e \u003cp\u003e16.3.1.2 Properties of Convex Functions 411\u003c\/p\u003e \u003cp\u003e16.3.2 Optimization and Role of Optimizer in ML 413\u003c\/p\u003e \u003cp\u003e16.3.2.1 Gradients-Descent Optimization Methods 414\u003c\/p\u003e \u003cp\u003e16.3.2.2 Non-Convexity of Cost Functions 416\u003c\/p\u003e \u003cp\u003e16.3.2.3 Basic Maths of SGD 418\u003c\/p\u003e \u003cp\u003e16.3.2.4 Saddle Points 418\u003c\/p\u003e \u003cp\u003e16.3.2.5 Gradient Pointing in the Wrong Direction 420\u003c\/p\u003e \u003cp\u003e16.3.2.6 Momentum-Based Optimization 423\u003c\/p\u003e \u003cp\u003e16.4 Conclusion and Discussion 424\u003c\/p\u003e \u003cp\u003eReferences 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges 429\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eS. Subasree and N. K. Sakthivel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 430\u003c\/p\u003e \u003cp\u003e17.2 Machine Learning and Deep Learning Framework 431\u003c\/p\u003e \u003cp\u003e17.2.1 Supervised Learning 433\u003c\/p\u003e \u003cp\u003e17.2.2 Unsupervised Learning 433\u003c\/p\u003e \u003cp\u003e17.2.3 Reinforcement Learning 434\u003c\/p\u003e \u003cp\u003e17.2.4 Deep Learning 434\u003c\/p\u003e \u003cp\u003e17.3 Challenges and Opportunities 435\u003c\/p\u003e \u003cp\u003e17.3.1 Literature Review 435\u003c\/p\u003e \u003cp\u003e17.4 Clinical Databases—Electronic Health Records 436\u003c\/p\u003e \u003cp\u003e17.5 Data Analytics Models—Classifiers and Clusters 436\u003c\/p\u003e \u003cp\u003e17.5.1 Criteria for Classification 438\u003c\/p\u003e \u003cp\u003e17.5.1.1 Probabilistic Classifier 439\u003c\/p\u003e \u003cp\u003e17.5.1.2 Support Vector Machines (SVMs) 439\u003c\/p\u003e \u003cp\u003e17.5.1.3 K-Nearest Neighbors 440\u003c\/p\u003e \u003cp\u003e17.5.2 Criteria for Clustering 441\u003c\/p\u003e \u003cp\u003e17.5.2.1 K-Means Clustering 442\u003c\/p\u003e \u003cp\u003e17.5.2.2 Mean Shift Clustering 442\u003c\/p\u003e \u003cp\u003e17.6 Deep Learning Approaches and Association Predictions 444\u003c\/p\u003e \u003cp\u003e17.6.1 G-HR: Gene Signature–Based HRF Cluster 444\u003c\/p\u003e \u003cp\u003e17.6.1.1 G-HR Procedure 446\u003c\/p\u003e \u003cp\u003e17.6.2 Deep Learning Approach and Association Predictions 446\u003c\/p\u003e \u003cp\u003e17.6.2.1 Deep Learning Approach 446\u003c\/p\u003e \u003cp\u003e17.6.2.2 Intelligent Human Disease-Gene Association Prediction Technique (IHDGAP) 447\u003c\/p\u003e \u003cp\u003e17.6.2.3 Convolution Neural Network 447\u003c\/p\u003e \u003cp\u003e17.6.2.4 Disease Semantic Similarity 449\u003c\/p\u003e \u003cp\u003e17.6.2.5 Computation of Scoring Matrix 450\u003c\/p\u003e \u003cp\u003e17.6.3 Identified Problem 450\u003c\/p\u003e \u003cp\u003e17.6.4 Deep Learning–Based Human Diseases Pattern Prediction Technique for High-Dimensional Human Diseases Datasets (ECNN-HDPT) 451\u003c\/p\u003e \u003cp\u003e17.6.5 Performance Analysis 453\u003c\/p\u003e \u003cp\u003e17.7 Conclusion 457\u003c\/p\u003e \u003cp\u003e17.8 Applications 458\u003c\/p\u003e \u003cp\u003eReferences 459\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Machine Learning and Deep Learning: Open Issues and Future Research Directions for the Next 10 Years 463\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAkshara Pramod, Harsh Sankar Naicker and Amit Kumar Tyagi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 464\u003c\/p\u003e \u003cp\u003e18.1.1 Comparison Among Data Mining, Machine Learning, and Deep Learning 465\u003c\/p\u003e \u003cp\u003e18.1.2 Machine Learning 465\u003c\/p\u003e \u003cp\u003e18.1.2.1 Importance of Machine Learning in Present Business Scenario 467\u003c\/p\u003e \u003cp\u003e18.1.2.2 Applications of Machine Learning 467\u003c\/p\u003e \u003cp\u003e18.1.2.3 Machine Learning Methods Used in Current Era 469\u003c\/p\u003e \u003cp\u003e18.1.3 Deep Learning 471\u003c\/p\u003e \u003cp\u003e18.1.3.1 Applications of Deep Learning 471\u003c\/p\u003e \u003cp\u003e18.1.3.2 Deep Learning Techniques\/Methods Used in Current Era 473\u003c\/p\u003e \u003cp\u003e18.2 Evolution of Machine Learning and Deep Learning 475\u003c\/p\u003e \u003cp\u003e18.3 The Forefront of Machine Learning Technology 476\u003c\/p\u003e \u003cp\u003e18.3.1 Deep Learning 476\u003c\/p\u003e \u003cp\u003e18.3.2 Reinforcement Learning 477\u003c\/p\u003e \u003cp\u003e18.3.3 Transfer Learning 477\u003c\/p\u003e \u003cp\u003e18.3.4 Adversarial Learning 477\u003c\/p\u003e \u003cp\u003e18.3.5 Dual Learning 478\u003c\/p\u003e \u003cp\u003e18.3.6 Distributed Machine Learning 478\u003c\/p\u003e \u003cp\u003e18.3.7 Meta Learning 478\u003c\/p\u003e \u003cp\u003e18.4 The Challenges Facing Machine Learning and Deep Learning 478\u003c\/p\u003e \u003cp\u003e18.4.1 Explainable Machine Learning 479\u003c\/p\u003e \u003cp\u003e18.4.2 Correlation and Causation 479\u003c\/p\u003e \u003cp\u003e18.4.3 Machine Understands the Known and is Aware of the Unknown 479\u003c\/p\u003e \u003cp\u003e18.4.4 People-Centric Machine Learning Evolution 480\u003c\/p\u003e \u003cp\u003e18.4.5 Explainability: Stems From Practical Needs and Evolves Constantly 480\u003c\/p\u003e \u003cp\u003e18.5 Possibilities With Machine Learning and Deep Learning 481\u003c\/p\u003e \u003cp\u003e18.5.1 Possibilities With Machine Learning 481\u003c\/p\u003e \u003cp\u003e18.5.1.1 Lightweight Machine Learning and Edge Computing 481\u003c\/p\u003e \u003cp\u003e18.5.1.2 Quantum Machine Learning 482\u003c\/p\u003e \u003cp\u003e18.5.1.3 Quantum Machine Learning Algorithms Based on Linear Algebra 482\u003c\/p\u003e \u003cp\u003e18.5.1.4 Quantum Reinforcement Learning 483\u003c\/p\u003e \u003cp\u003e18.5.1.5 Simple and Elegant Natural Laws 483\u003c\/p\u003e \u003cp\u003e18.5.1.6 Improvisational Learning 484\u003c\/p\u003e \u003cp\u003e18.5.1.7 Social Machine Learning 485\u003c\/p\u003e \u003cp\u003e18.5.2 Possibilities With Deep Learning 485\u003c\/p\u003e \u003cp\u003e18.5.2.1 Quantum Deep Learning 485\u003c\/p\u003e \u003cp\u003e18.6 Potential Limitations of Machine Learning and Deep Learning 486\u003c\/p\u003e \u003cp\u003e18.6.1 Machine Learning 486\u003c\/p\u003e \u003cp\u003e18.6.2 Deep Learning 487\u003c\/p\u003e \u003cp\u003e18.7 Conclusion 488\u003c\/p\u003e \u003cp\u003eAcknowledgement 489\u003c\/p\u003e \u003cp\u003eContribution\/Disclosure 489\u003c\/p\u003e \u003cp\u003eReferences 489\u003c\/p\u003e \u003cp\u003eIndex 491\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407151014231,"sku":"9781119785729","price":169.16,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/computational-analysis-and-deep-learning-for-medical-care-9781119785729","provider":"Book Curl","version":"1.0","type":"link"}