Preface xix -- Part I: Deep Learning and Its Models 1 -- 1 CNN: A Review of Models, Application of IVD Segmentation 3 Leena 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 35 R. 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 53 B. 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 79 Suresh 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 109 Amit 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 131 K. 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 155 Giriraj 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 181 Neetu 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 217 Aswathy 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 239 Nikita 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 257 Diksha 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 -- ...