Computational Modelling and Imaging for SARS-CoV-2 and COVID-19

Description

The aim of this book is to present new computational techniques and methodologies for the analysis of the clinical, epidemiological and public health aspects of SARS-CoV-2 and COVID-19 pandemic. The book presents the use of soft computing techniques such as machine learning algorithms for analysis of the epidemiological aspects of the SARS-CoV-2. This book clearly explains novel computational image processing algorithms for the detection of COVID-19 lesions in lung CT and X-ray images. It explores various computational methods for computerized analysis of the SARS-CoV-2 infection including severity assessment. The book provides a detailed description of the algorithms which can potentially aid in mass screening of SARS-CoV-2 infected cases. Finally the book also explains the conventional epidemiological models and machine learning techniques for the prediction of the course of the COVID-19 epidemic. It also provides real life examples through case studies. The book is intended for biomedical engineers, mathematicians, postgraduate students; researchers; medical scientists working on identifying and tracking infectious diseases. Cover Half Title Title Page Copyright Page Contents Preface Editors Contributors 1. Artificial Intelligence Based COVID-19 Detection using Medical Imaging Methods: A Review 1.1 Introduction 1.1.1 Statistics 1.1.2 Clinical Symptoms, Manifestations and their Effects 1.2 Diagnosis Methods and Need for an AI-based Solution 1.3 Artificial Intelligence Methods 1.4 Datasets 1.5 Related Research 1.5.1 CT Scan Images based COVID-19 Detection using AI Methods 1.5.2 X-Ray Images based COVID-19 Detection using AI Methods 1.6 Conclusion Acknowledgement References 2. Review on Imaging Features for COVID-19 2.1 Introduction 2.2 Review of Literature 2.3 Diagnosis 2.3.1 RT-PCR Test 2.3.2 Chest Radiography 2.3.3 PET/CT 2.3.4 Magnetic Resonance Imaging (MRI) 2.3.5 Ultrasonography 2.3.6 Chest Computed Tomography (CT) 2.4 Prevention mechanisms 2.5 Discussion 2.6 Conclusion References 3. Investigation of COVID-19 Chest X-ray Images using Texture Features - A Comprehensive Approach 3.1 Introduction 3.2 Methodology 3.2.1 Database 3.2.2 Materials and Methods 3.3 Results and Discussion 3.4 Conclusion References 4. Efficient Diagnosis using Chest CT in COVID-19: A Review 4.1 Introduction 4.2 Clinical Evaluations 4.3 Image Interpretations 4.4 Conclusion References 5. Automatic Mask Detection and Social Distance Alerting Based on a Deep-Learning Computer Vision Algorithm 5.1 Introduction 5.2 Convolutional Neural Network 5.3 Region Proposal-based Framework 5.4 Bounding Box Regression Principle 5.5 Proposal Layer 5.6 Faster RCNN Training 5.7 Need of GPU Cloud 5.8 Tensorflow Object Detection (TFOD) 5.9 Configuration Steps for Tensor Flow Object Detection 5.10 Results and Analysis 5.11 Conclusion and Future Scope References 6. Review of Effective Mathematical Modelling of Coronavirus Epidemic and Effect of drone Disinfection 6.1 Introduction 6.2 Methodology 6.3 Thermal Imaging 6.4 Broadcasting Information 6.5 Delivery of Essentials 6.6 Patrolling 6.7 Disinfection 6.8 Results and Discussion 6.9 Conclusion References 7. ANFIS Algorithm-based Modeling and Forecasting of the COVID-19 Epidemic: A Case Study in Tamil Nadu, India 7.1 Introduction 7.2 Computational Methods 7.3 Mathematical Modeling of COVID-19 Pandemic 7.4 Adaptive Neuro Fuzzy Inference System (ANFIS) 7.5 Forward Modeling of COVID-19 using ANFIS 7.6 Simulation Study of ANFIS Models for Epidemic Cases in the State of Tamil Nadu, India 7.7 The Prediction of Tamil Nadu Province Epidemic 7.7.1 Epidemic Transmission 7.7.2 Active Cases 7.7.3 Fatality Rate 7.8 Conclusion Acknowledgments References 8. Prediction and Analysis of SARS-CoV-2 (COVID-19) epidemic in India using LSTM Network 8.1 Introduction 8.2 Data Source 8.3 Current Scenario of SARS-CoV-2 (COVID-19) in India 8.4 Study Daily Infection and Death Rates in States 8.5 Methods-LSTM Network Model Using Python 8.6 LSTM Network Implementation 8.7 Moving Average 8.8 Results and Discussion 8.9 Conclusion References Index