Ch.1. Applying Deep Learning and Emerging
Technologies in Combating COVID-19
Ch. 2. COVID-19 Detection
from Chest Radiographs Using Machine Learning and Convolutional Neural Networks
Ch. 3. Inf-Net:
An Automatic Lung Infection Segmentation Network from CT Images
Ch. 4. A Comprehensive Review on Radiology Smartphone Applications
Ch. 5. A Hybrid
Deep Learning Method with Attention to Forecast COVID-19 Spread
Ch.
6. A Residual Network Based Deep Learning Model for Detection of COVID-19 from
Cough Sounds
Ch.
7. AI-based COVID-19 Diagnosis Among Eight Other Lung Respiratory Diseases:
Rapid and Accurate
Ch.
8. Diagnosis of COVID-19 Based on Support Vector
Machine by Feature Selection Techniques
Ch. 9. Post-Analysis of COVID-19
Pneumonia Based on Chest CT Images Using AI algorithms: A Clinical Point of
View
Ch. 10. Lung CT Scans for Management of Pneumonitis
and Diagnosis in COVID-19
Ch. 11. Applications of Machine
Learning in COVID-19 Pandemic: A Scoping Review

Coronavirus disease 2019 (COVID-19), was initially detected in Wuhan, China in 2019, and is caused by a novel RNA virus belonging to the Coronaviridae family. For most patients who have died of COVID-19, the ultimate cause of death was pneumonia. Severe pneumonia often requires lengthy hospital stays in intensive care units and assistance breathing with ventilators-medical devices now in high demand. To quickly detect pneumonia-and therefore better distinguish between COVID-19 patients likely to need more supportive care in hospitals and those who could be followed closely at home, a number of radiologists and physicians at leading medical centres world-wide are now using artificial intelligence (AI) to augment lung imaging analysis. The utilization of AI strategies for diagnosis, follow-up, and treatments of COVID-19 patients is now becoming essential.

This is the first comprehensive reference work published detailing the latest research and developments in the utilization of AI strategies in the diagnosis and treatment of COVID-19 patients. The first section details aspects of the early assessment of lung functions in coronavirus patients. The second section relates to the incorporation of AI and Machine Learning paradigms. Referencing a wealth of data that has been collected on COVID-19 particularly from an imaging standpoint, this book is important for academics, clinicians and scientists working in the domain of lung cancer, data-mining, machine learning, and deep learning within the COVID-19 environment.

Ayman El-Baz is a Distinguished Professor at University of Louisville, Kentucky, United States and University of Louisville at AlAlamein International University (UofL-AIU), New Alamein City, Egypt. Dr. El-Baz earned his B.Sc. and M.Sc. degrees in electrical engineering in 1997 and 2001, respectively. He earned his Ph.D. in electrical engineering from the University of Louisville in 2006. Dr. El-Baz was named as a Fellow for Coulter, AIMBE and NAI for his contributions to the field of biomedical translational research. Dr. El-Baz has almost two decades of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or co-authored more than 500 technical articles (182 journals, 44 books, 95 book chapters, 253 refereed-conference papers, 215 abstracts, and 38 US patents and Disclosures).

Jasjit S. Suri is an innovator, scientist, visionary, industrialist and an internationally known world leader in biomedical engineering. Dr. Suri has spent over 25 years in the field of biomedical engineering/devices and its management. He received his Ph.D. from the University of Washington, Seattle and his Business Management Sciences degree from Weatherhead, Case Western Reserve University, Cleveland, Ohio. Dr. Suri was crowned with President's Gold medal in 1980 and made Fellow of the American Institute of Medical and Biological Engineering for his outstanding contributions. In 2018, he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management .