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 book references a wealth of data that has been collected on COVID-19 particularly from an imaging standpoint, with the first section detailing aspects of the early assessment of lung functions in coronavirus patients, and the second section relating to the incorporation of AI and Machine Learning paradigms. 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.
Key Features:
- Comprehensive overview of the implementation of artificial intelligence (AI) and machine learning (ML) strategies to the diagnosis, follow on/follow-up, and treatments of COVID-19 patients.
- A number of authors/contributors are front-line researchers and clinicians in COVID-19 patient affairs, representing the USA, India, UK, France and importantly China.
- Provides unique coverage of AI and Machine Learning in the prediction of blood-clotting in COVID-19 patients.
- Offers specific examples of diagnosing COVID-19 with AI utilization within CT.
- Presents extensive references at the end of each chapter to enhance further study.
Inhaltsverzeichnis
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 ApplicationsCh. 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
Über den Autor
Ayman El-Baz is a Distinguished Professor at University of Louisville, Kentucky, United States and University of Louisville at Al Alamein International University (Uof L-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 .
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