Preface xix Part I: Deep Learning and Its Models 1 1 CNN: A Review of Models, Application of IVD Segmentation 3Leena Silvoster M. and R. Mathusoothana S. Kumar 1.1 Introduction 4 1.2 Various CNN Models 4 1.2.1 LeNet-5 4 1.2.2 AlexNet 7 1.2.3 ZFNet 8 1.2.4 VGGNet 10 1.2.5 GoogLeNet 12 1.2.6 ResNet 16 1.2.7 ResNeXt 21 1.2.8 SE-ResNet 24 1.2.9 DenseNet 24 1.2.10 MobileNets 25 1.3 Application of CNN to IVD Detection 26 1.4 Comparison With State-of-the-Art Segmentation Approaches for Spine T2W Images 28 1.5 Conclusion 28 References 33 2 Location-Aware Keyword Query Suggestion Techniques With Artificial Intelligence Perspective 35R. Ravinder Reddy, C. Vaishnavi, Ch. Mamatha and S. Ananthakumaran 2.1 Introduction 36 2.2 Related Work 39 2.3 Artificial Intelligence Perspective 41 2.3.1 Keyword Query Suggestion 42 2.3.1.1 Random Walk–Based Approaches 42 2.3.1.2 Cluster-Based Approaches 42 2.3.1.3 Learning to Rank Approaches 43 2.3.2 User Preference From Log 43 2.3.3 Location-Aware Keyword Query Suggestion 44 2.3.4 Enhancement With AI Perspective 44 2.3.4.1 Case Study 45 2.4 Architecture 46 2.4.1 Distance Measures 47 2.5 Conclusion 49 References 49 3 Identification of a Suitable Transfer Learning Architecture for Classification: A Case Study with Liver Tumors 53B. Lakshmi Priya, K. Jayanthi, Biju Pottakkat and G. Ramkumar 3.1 Introduction 54 3.2 Related Works 56 3.3 Convolutional Neural Networks 58 3.3.1 Feature Learning in CNNs 59 3.3.2 Classification in CNNs 60 3.4 Transfer Learning 61 3.4.1 AlexNet 61 3.4.2 GoogLeNet 62 3.4.3 Residual Networks 63 3.4.3.1 ResNet-18 65 3.4.3.2 ResNet-50 65 3.5 System Model 66 3.6 Results and Discussions 67 3.6.1 Dataset 67 3.6.2 Assessment of Transfer Learning Architectures 67 3.7 Conclusion 73 References 74 4 Optimization and Deep Learning-Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images 79Suresh Kumar K., Sundaresan S., Nishanth R. and Ananth Kumar T. 4.1 Introduction 80 4.2 Related Works 82 4.3 Proposed Method 85 4.3.1 Input Dataset 86 4.3.2 Pre-Processing 86 4.3.3 Combination of DCNN and CFML 86 4.3.4 Fine Tuning and Optimization 88 4.3.5 Feature Extraction 89 4.3.6 Localization of Abnormalities in MRI and CT Scanned Images 90 4.4 Results and Discussion 92 4.4.1 Metric Learning 92 4.4.2 Comparison of the Various Models for Image Retrieval 92 4.4.3 Precision vs. Recall Parameters Estimation for the CBIR 93 4.4.4 Convolutional Neural Networks–Based Landmark Localization 96 4.5 Conclusion 104 References 104 Part II: Applications of Deep Learning 107 5 Deep Learning for Clinical and Health Informatics 109Amit Kumar Tyagi and Meghna Mannoj Nair 5.1 Introduction 110 5.1.1 Deep Learning Over Machine Learning 111 5.2 Related Work 113 5.3 Motivation 115 5.4 Scope of the Work in Past, Present, and Future 115 5.5 Deep Learning Tools, Methods Available for Clinical, and Health Informatics 117 5.6 Deep Learning: Not-So-Near Future in Biomedical Imaging 119 5.6.1 Types of Medical Imaging 119 5.6.2 Use and Benefits of Medical Imaging 120 5.7 Challenges Faced Toward Deep Learning Using Biomedical Imaging 121 5.7.1 Deep Learning in Healthcare: Limitations and Challenges 122 5.8 Open Research Issues and Future Research Directions Biomedical Imaging (Healthcare Informatics) 124 5.9 Conclusion 127 References 127 6 Biomedical Image Segmentation by Deep Learning Methods 131K. Anita Davamani, C.R. Rene Robin, S. Amudha and L. Jani Anbarasi 6.1 Introduction 132 6.2 Overview of Deep Learning Algorithms 135 6.2.1 Deep Learning Classifier (DLC) 136 6.2.2 Deep Learning Architecture 137 6.3 Other Deep Learning Architecture 139 6.3.1 Restricted Boltzmann Machine (RBM) 139 6.3.2 Deep Learning Architecture Containing Autoencoders 140 6.3.3 Sparse Coding Deep Learning Architecture 141 6.3.4 Generative Adversarial Network (GAN) 141 6.3.5 Recurrent Neural Network (RNN) 141 6.4 Biomedical Image Segmentation 145 6.4.1 Clinical Images 146 6.4.2 X-Ray Imaging 146 6.4.3 Computed Tomography (CT) 147 6.4.4 Magnetic Resonance Imaging (MRI) 147 6.4.5 Ultrasound Imaging (US) 148 6.4.6 Optical Coherence Tomography (OCT) 148 6.5 Conclusion 149 References 149 7 Multi-Lingual Handwritten Character Recognition Using Deep Learning 155Giriraj Parihar, Ratnavel Rajalakshmi and Bhuvana J. 7.1 Introduction 156 7.2 Related Works 157 7.3 Materials and Methods 160 7.4 Experiments and Results 161 7.4.1 Dataset Description 162 7.4.1.1 Handwritten Math Symbols 162 7.4.1.2 Bangla Handwritten Character Dataset 162 7.4.1.3 Devanagari Handwritten Character Dataset 162 7.4.2 Experimental Setup 162 7.4.3 Hype-Parameters 164 7.4.3.1 English Model 164 7.4.3.2 Hindi Model 165 7.4.3.3 Bangla Model 165 7.4.3.4 Math Symbol Model 165 7.4.3.5 Combined Model 166 7.4.4 Results and Discussion 167 7.4.4.1 Performance of Uni-Language Models 167 7.4.4.2 Uni-Language Model on English Dataset 168 7.4.4.3 Uni-Language Model on Hindi Dataset 168 7.4.4.4 Uni-Language Model on Bangla Dataset 169 7.4.4.5 Uni-Language Model on Math Symbol Dataset 169 7.4.4.6 Performance of Multi-Lingual Model on Combined Dataset 171 7.5 Conclusion 177 References 178 8 Disease Detection Platform Using Image Processing Through OpenCV 181Neetu Faujdar and Aparna Sinha 8.1 Introduction 182 8.1.1 Image Processing 183 8.2 Problem Statement 183 8.2.1 Cataract 183 8.2.1.1 Causes 184 8.2.1.2 Types of Cataracts 184 8.2.1.3 Cataract Detection 185 8.2.1.4 Treatment 186 8.2.1.5 Prevention 186 8.2.1.6 Methodology 186 8.2.2 Eye Cancer 192 8.2.2.1 Symptoms 194 8.2.2.2 Causes of Retinoblastoma 194 8.2.2.3 Phases 195 8.2.2.4 Spreading of Cancer 196 8.2.2.5 Diagnosis 196 8.2.2.6 Treatment 197 8.2.2.7 Methodology 199 8.2.3 Skin Cancer (Melanoma) 202 8.2.3.1 Signs and Symptoms 203 8.2.3.2 Stages 203 8.2.3.3 Causes of Melanoma 204 8.2.3.4 Diagnosis 204 8.2.3.5 Treatment 205 8.2.3.6 Methodology 206 8.2.3.7 Asymmetry 207 8.2.3.8 Border 208 8.2.3.9 Color 208 8.2.3.10 Diameter Detection 209 8.2.3.11 Calculating TDS (Total Dermoscopy Score) 210 8.3 Conclusion 210 8.4 Summary 212 References 212 9 Computer-Aided Diagnosis of Liver Fibrosis in Hepatitis Patients Using Convolutional Neural Network 217Aswathy S. U., Ajesh F., Shermin Shamsudheen and Jarin T. 9.1 Introduction 218 9.2 Overview of System 219 9.3 Methodology 219 9.3.1 Dataset 220 9.3.2 Pre-Processing 221 9.3.3 Feature Extraction 221 9.3.4 Feature Selection and Normalization 223 9.3.5 Classification Model 225 9.4 Performance and Analysis 227 9.5 Experimental Results 232 9.6 Conclusion and Future Scope 232 References 233 Part III: Future Deep Learning Models 237 10 Lung Cancer Prediction in Deep Learning Perspective 239Nikita Banerjee and Subhalaxmi Das 10.1 Introduction 239 10.2 Machine Learning and Its Application 240 10.2.1 Machine Learning 240 10.2.2 Different Machine Learning Techniques 241 10.2.2.1 Decision Tree 242 10.2.2.2 Support Vector Machine 242 10.2.2.3 Random Forest 242 10.2.2.4 K-Means Clustering 242 10.3 Related Work 243 10.4 Why Deep Learning on Top of Machine Learning? 245 10.4.1 Deep Neural Network 246 10.4.2 Deep Belief Network 247 10.4.3 Convolutional Neural Network 247 10.5 How is Deep Learning Used for Prediction of Lungs Cancer? 248 10.5.1 Proposed Architecture 248 10.5.1.1 Pre-Processing Block 250 10.5.1.2 Segmentation 250 10.5.1.3 Classification 252 10.6 Conclusion 253 References 253 11 Lesion Detection and Classification for Breast Cancer Diagnosis Based on Deep CNNs from Digital Mammographic Data 257Diksha Rajpal, Sumita Mishra and Anil Kumar 11.1 Introduction 257 11.2 Background 258 11.2.1 Methods of Diagnosis of Breast Cancer 258 11.2.2 Types of Breast Cancer 260 11.2.3 Breast Cancer Treatment Options 261 11.2.4 Limitations and Risks of Diagnosis and Treatment Options 262 11.2.4.1 Limitation of Diagnosis Methods 262 11.2.4.2 Limitations of Treatment Plans 263 11.2.5 Deep Learning Methods for Medical Image Analysis: Tumor Classification 263 11.3 Methods 265 11.3.1 Digital Repositories 265 11.3.1.1 DDSM Database 265 11.3.1.2 AMDI Database 265 11.3.1.3 IRMA Database 265 11.3.1.4 BreakHis Database 265 11.3.1.5 MIAS Database 266 11.3.2 Data Pre-Processing 266 11.3.2.1 Advantages of Pre-Processing Images 267 11.3.3 Convolutional Neural Networks (CNNs) 268 11.3.3.1 Architecture of CNN 269 11.3.4 Hyper-Parameters 272 11.3.4.1 Number of Hidden Layers 273 11.3.4.2 Dropout Rate 273 11.3.4.3 Activation Function 273 11.3.4.4 Learning Rate 274 11.3.4.5 Number of Epochs 274 11.3.4.6 Batch Size 274 11.3.5 Techniques to Improve CNN Performance 274 11.3.5.1 Hyper-Parameter Tuning 274 11.3.5.2 Augmenting Images 274 11.3.5.3 Managing Over-Fitting and Under-Fitting 275 11.4 Application of Deep CNN for Mammography 275 11.4.1 Lesion Detection and Localization 275 11.4.2 Lesion Classification 279 11.5 System Model and Results 280 11.5.1 System Model 280 11.5.2 System Flowchart 281 11.5.2.1 MIAS Database 281 11.5.2.2 Unannotated Images 281 11.5.3 Results 282 11.5.3.1 Distribution and Processing of Dataset 282 11.5.3.2 Training of the Model 283 11.5.3.3 Prediction of Unannotated Images 286 11.6 Research Challenges and Discussion on Future Directions 286 11.7 Conclusion 288 References 289 12 Health Prediction Analytics Using Deep Learning Methods and Applications 293Sapna Jain, M. Afshar Alam, Nevine Makrim Labib and Eiad Yafi 12.1 Introduction 294 12.2 Background 298 12.3 Predictive Analytics 299 12.4 Deep Learning Predictive Analysis Applications 305 12.4.1 Deep Learning Application Model to Predict COVID-19 Infection 305 12.4.2 Deep Transfer Learning for Mitigating the COVID-19 Pandemic 308 12.4.3 Health Status Prediction for the Elderly Based on Machine Learning 309 12.4.4 Deep Learning in Machine Health Monitoring 311 12.5 Discussion 319 12.6 Conclusion 320 References 321 13 Ambient-Assisted Living of Disabled Elderly in an Intelligent Home Using Behavior Prediction—A Reliable Deep Learning Prediction System 329Sophia S., Sridevi U.K., Boselin Prabhu S.R. and P. Thamaraiselvi 13.1 Introduction 330 13.2 Activities of Daily Living and Behavior Analysis 331 13.3 Intelligent Home Architecture 333 13.4 Methodology 335 13.4.1 Record the Behaviors Using Sensor Data 335 13.4.2 Classify Discrete Events and Relate the Events Using Data Analysis Algorithms 335 13.4.3 Construct Behavior Dictionaries for Flexible Event Intervals Using Deep Learning Concepts 335 13.4.4 Use the Dictionary in Modeling the Behavior Patterns Through Prediction Techniques 336 13.4.5 Detection of Deviations From Expected Behaviors Aiding the Automated Elderly Monitoring Based on Decision Support Algorithm Systems 336 13.5 Senior Analytics Care Model 337 13.6 Results and Discussions 338 13.7 Conclusion 341 Nomenclature 341 References 342 14 Early Diagnosis Tool for Alzheimer’s Disease Using 3D Slicer 343V. Krishna Kumar, M.S. Geetha Devasena and G. Gopu 14.1 Introduction 344 14.2 Related Work 345 14.3 Existing System 347 14.4 Proposed System 347 14.4.1 Usage of 3D Slicer 350 14.5 Results and Discussion 353 14.6 Conclusion 356 References 356 Part IV: Deep Learning – Importance and Challenges for Other Sectors 361 15 Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities 363Meenu Gupta, Akash Gupta and Gaganjot Kaur 15.1 Introduction 364 15.2 Related Work 365 15.3 Development of Personalized Medicine Using Deep Learning: A New Revolution in Healthcare Industry 367 15.3.1 Deep Feedforward Neural Network (DFF) 367 15.3.2 Convolutional Neural Network 367 15.3.3 Recurrent Neural Network (RNN) 369 15.3.4 Long/Short-Term Memory (LSTM) 369 15.3.5 Deep Belief Network (DBN) 370 15.3.6 Autoencoder (AE) 370 15.4 Deep Learning Applications in Precision Medicine 370 15.4.1 Discovery of Biomarker and Classification of Patient 370 15.4.2 Medical Imaging 371 15.5 Deep Learning for Medical Imaging 372 15.5.1 Medical Image Detection 372 15.5.1.1 Pathology Detection 372 15.5.1.2 Detection of Image Plane 373 15.5.1.3 Anatomical Landmark Localization 373 15.5.2 Medical Image Segmentation 373 15.5.2.1 Supervised Algorithms 374 15.5.2.2 Semi-Supervised Algorithms 374 15.5.3 Medical Image Enhancement 375 15.5.3.1 Two-Dimensional Super-Resolution Techniques 375 15.5.3.2 Three-Dimensional Super-Resolution Techniques 375 15.6 Drug Discovery and Development: A Promise Fulfilled by Deep Learning Technology 375 15.6.1 Prediction of Drug Properties 376 15.6.2 Prediction of Drug-Target Interaction 377 15.7 Application Areas of Deep Learning in Healthcare 377 15.7.1 Medical Chatbots 377 15.7.2 Smart Health Records 377 15.7.3 Cancer Diagnosis 378 15.8 Privacy Issues Arising With the Usage of Deep Learning in Healthcare 379 15.8.1 Private Data 379 15.8.2 Privacy Attacks 380 15.8.2.1 Evasion Attack 380 15.8.2.2 White-Box Attack 380 15.8.2.3 Black-Box Attack 380 15.8.2.4 Poisoning Attack 381 15.8.3 Privacy-Preserving Techniques 381 15.8.3.1 Differential Privacy With Deep Learning 381 15.8.3.2 Homomorphic Encryption (HE) on Deep Learning 382 15.8.3.3 Secure Multiparty Computation on Deep Learning 383 15.9 Challenges and Opportunities in Healthcare Using Deep Learning 383 15.10 Conclusion and Future Scope 386 References 387 16 A Perspective Analysis of Regularization and Optimization Techniques in Machine Learning 393Ajeet K. Jain, PVRD Prasad Rao and K. Venkatesh Sharma 16.1 Introduction 394 16.1.1 Data Formats 395 16.1.1.1 Structured Data 395 16.1.1.2 Unstructured Data 396 16.1.1.3 Semi-Structured Data 396 16.1.2 Beginning With Learning Machines 397 16.1.2.1 Perception 397 16.1.2.2 Artificial Neural Network 398 16.1.2.3 Deep Networks and Learning 399 16.1.2.4 Model Selection, Over-Fitting, and Under-Fitting 400 16.2 Regularization in Machine Learning 402 16.2.1 Hamadard Conditions 403 16.2.2 Tikhonov Generalized Regularization 404 16.2.3 Ridge Regression 406 16.2.4 Lasso—L1 Regularization 406 16.2.5 Dropout as Regularization Feature 407 16.2.6 Augmenting Dataset 408 16.2.7 Early Stopping Criteria 408 16.3 Convexity Principles 409 16.3.1 Convex Sets 410 16.3.1.1 Affine Set and Convex Functions 411 16.3.1.2 Properties of Convex Functions 411 16.3.2 Optimization and Role of Optimizer in ML 413 16.3.2.1 Gradients-Descent Optimization Methods 414 16.3.2.2 Non-Convexity of Cost Functions 416 16.3.2.3 Basic Maths of SGD 418 16.3.2.4 Saddle Points 418 16.3.2.5 Gradient Pointing in the Wrong Direction 420 16.3.2.6 Momentum-Based Optimization 423 16.4 Conclusion and Discussion 424 References 425 17 Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges 429S. Subasree and N. K. Sakthivel 17.1 Introduction 430 17.2 Machine Learning and Deep Learning Framework 431 17.2.1 Supervised Learning 433 17.2.2 Unsupervised Learning 433 17.2.3 Reinforcement Learning 434 17.2.4 Deep Learning 434 17.3 Challenges and Opportunities 435 17.3.1 Literature Review 435 17.4 Clinical Databases—Electronic Health Records 436 17.5 Data Analytics Models—Classifiers and Clusters 436 17.5.1 Criteria for Classification 438 17.5.1.1 Probabilistic Classifier 439 17.5.1.2 Support Vector Machines (SVMs) 439 17.5.1.3 K-Nearest Neighbors 440 17.5.2 Criteria for Clustering 441 17.5.2.1 K-Means Clustering 442 17.5.2.2 Mean Shift Clustering 442 17.6 Deep Learning Approaches and Association Predictions 444 17.6.1 G-HR: Gene Signature–Based HRF Cluster 444 17.6.1.1 G-HR Procedure 446 17.6.2 Deep Learning Approach and Association Predictions 446 17.6.2.1 Deep Learning Approach 446 17.6.2.2 Intelligent Human Disease-Gene Association Prediction Technique (IHDGAP) 447 17.6.2.3 Convolution Neural Network 447 17.6.2.4 Disease Semantic Similarity 449 17.6.2.5 Computation of Scoring Matrix 450 17.6.3 Identified Problem 450 17.6.4 Deep Learning–Based Human Diseases Pattern Prediction Technique for High-Dimensional Human Diseases Datasets (ECNN-HDPT) 451 17.6.5 Performance Analysis 453 17.7 Conclusion 457 17.8 Applications 458 References 459 18 Machine Learning and Deep Learning: Open Issues and Future Research Directions for the Next 10 Years 463Akshara Pramod, Harsh Sankar Naicker and Amit Kumar Tyagi 18.1 Introduction 464 18.1.1 Comparison Among Data Mining, Machine Learning, and Deep Learning 465 18.1.2 Machine Learning 465 18.1.2.1 Importance of Machine Learning in Present Business Scenario 467 18.1.2.2 Applications of Machine Learning 467 18.1.2.3 Machine Learning Methods Used in Current Era 469 18.1.3 Deep Learning 471 18.1.3.1 Applications of Deep Learning 471 18.1.3.2 Deep Learning Techniques/Methods Used in Current Era 473 18.2 Evolution of Machine Learning and Deep Learning 475 18.3 The Forefront of Machine Learning Technology 476 18.3.1 Deep Learning 476 18.3.2 Reinforcement Learning 477 18.3.3 Transfer Learning 477 18.3.4 Adversarial Learning 477 18.3.5 Dual Learning 478 18.3.6 Distributed Machine Learning 478 18.3.7 Meta Learning 478 18.4 The Challenges Facing Machine Learning and Deep Learning 478 18.4.1 Explainable Machine Learning 479 18.4.2 Correlation and Causation 479 18.4.3 Machine Understands the Known and is Aware of the Unknown 479 18.4.4 People-Centric Machine Learning Evolution 480 18.4.5 Explainability: Stems From Practical Needs and Evolves Constantly 480 18.5 Possibilities With Machine Learning and Deep Learning 481 18.5.1 Possibilities With Machine Learning 481 18.5.1.1 Lightweight Machine Learning and Edge Computing 481 18.5.1.2 Quantum Machine Learning 482 18.5.1.3 Quantum Machine Learning Algorithms Based on Linear Algebra 482 18.5.1.4 Quantum Reinforcement Learning 483 18.5.1.5 Simple and Elegant Natural Laws 483 18.5.1.6 Improvisational Learning 484 18.5.1.7 Social Machine Learning 485 18.5.2 Possibilities With Deep Learning 485 18.5.2.1 Quantum Deep Learning 485 18.6 Potential Limitations of Machine Learning and Deep Learning 486 18.6.1 Machine Learning 486 18.6.2 Deep Learning 487 18.7 Conclusion 488 Acknowledgement 489 Contribution/Disclosure 489 References 489 Index 491

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