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This book presents 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.
Inhaltsverzeichnis
1. Artificial Intelligence Based CoVID-19 Detection using Medical Imaging Methods: A Review. 2. Review on Imaging Features for COVID-19. 3. Investigation of COVID-19 Chest X-ray Images using Texture Features –A Comprehensive Approach. 4. Efficient Diagnosis using Chest CT in COVID-19: A Review. 5. Automatic Mask Detection and Social Distance Alerting Based on a Deep Learning Computer Vision Algorithm. 6. Review of Effective Mathematical Modelling of Coronavirus Epidemic and Effect of Drone Disinfection. 7. ANFIS Algorithm based Modeling and Forecasting of the COVID-19 Epidemic: A Case Study in Tamil Nadu, India. 8. Prediction and Analysis of SARS-CoV-2 (COVID-19) Epidemic in India using LSTM Network
Über den Autor / die Autorin
Dr. S. Prabha is an Associate Professor at the Department of Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Chennai, India.
Dr. P. Karthikeyan is an Assistant Professor in the Department of Production Technology, Madras Institute of Technology, Anna University, India.
Dr. K. Kamalanand is an Assistant Professor at the Department of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai, India.
Dr. N. Selvaganesan is Professor of Department of Avionics in IIST-Trivandrum.
Zusammenfassung
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.
