Table of Contents
1 Cover
4 Preface
5 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 1.3 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 2.4 Architecture 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 3.4 Transfer Learning 3.5 System Model 3.6 Results and Discussions 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 4.4 Results and Discussion 4.5 Conclusion References
6
Part 2: Applications of Deep Learning
5 Deep Learning for Clinical and Health Informatics
5.1 Introduction
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
5.7 Challenges Faced Toward Deep Learning Using in Biomedical Imaging
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
6.3 Other Deep Learning Architecture
6.4 Biomedical Image Segmentation
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
7.5 Conclusion
References
8 Disease Detection Platform Using Image Processing Through OpenCV
8.1 Introduction
8.2 Problem Statement
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
9.4 Performance and Analysis
9.5