Computational analysis and deep learning for medical care : principles, methods, and applications /: principles, methods, and applications. (2021)

• Record Type: Book
• Further Information: Note: Edited by Amit Kumar Tyagi.
• Editors: Tyagi, Amit Kumar
• Contents: 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&l
• Edition: 1st
• Publisher Details: Hoboken : Wiley-Scrivener
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 610.28563; Artificial intelligence -- Medical applications; Imaging systems in medicine; Neural networks (Computer science)
• Languages: English
• ISBNs: 9781119785736
• Related ISBNs: 9781119785729
• Notes: Note: Description based on CIP data; resource not viewed.
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