Artificial intelligence strategies for analyzing COVID-19 pneumonia lung imaging. Volume 1, Characterization approaches / edited by Ayman El-Baz, Jasjit S. Suri.
Contributor(s): El-Baz, Ayman S [editor.]
| Suri, Jasjit S [editor.] | Institute of Physics (Great Britain) [publisher.].
Material type: 
BookSeries: IOP (Series)Release 22: ; IOP ebooks2022 collection: Publisher: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2022]Description: 1 online resource (various pagings) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9780750337953; 9780750337946.Subject(s): COVID-19 (Disease) -- Patients -- Treatment | Viral pneumonia | Lungs -- Diseases -- Imaging | Artificial intelligence -- Medical applications | COVID-19 -- therapy | Pneumonia, Viral | Lung -- diagnostic imaging | Artificial Intelligence | Medicine | Medical physics and biophysicsAdditional physical formats: Print version:: No titleDDC classification: 616.2/4140285 Online resources: Click here to access online Also available in print."Version: 20220401"--Title page verso.
Includes bibliographical references.
1. Applying deep learning and emerging technologies in combating COVID-19 / Adedoyin A. Hussain and Barakat A. Dawood -- 2. Detecting COVID-19 in chest radiographs using machine learning and convolutional neural networks / Andrew C. Li, David T. Lee, Kristoff K. Misquitta, Kaiji Uno and Sasha Wald -- 3. Inf-Net : an automatic lung infection segmentation network that uses CT images / Tao Zhou, Dengping Fan, Gepeng Ji, Geng Chen, Huazhu Fu and Ling Shao -- 4. A comprehensive review of radiology smartphone applications / Mohammad (Behdad) Jamshidi, Mohammad Heydari, Jakub Talla, Zden�eek Peroutka, Maryam Ahmadi, Esmaeil Mehraeen, Hamid Abdollahi and Amirali Karimi -- 5. A hybrid deep learning method with attention for COVID-19 spread forecasting / Abdelkader Dairi, Fouzi Harrou, Ying Sun and Sofiane Khadraoui -- 6. A residual network-based deep learning model for the detection of COVID-19 using cough sounds / Annesya Banerjee and Achal Nilhani -- 7. Rapid and accurate AI-based diagnosis to distinguish COVID-19 from eight other lung respiratory diseases / Mugahed A. Al-Antari, Cam-Hao Hua and Sungyoung Lee -- 8. Diagnosis of COVID-19 by feature selection techniques using support vector machine / Amira M. Hasan, Hala M. Abd El-Kader and Aya Hossam -- 9. Post-analysis of COVID-19 pneumonia based on chest CT images using AI algorithms : a clinical point of view / M. Parimala Devi, G. Boopathi Raja, T. Sathya and V. Gowrishankar -- 10. Lung CT scans for the management of COVID-19 pneumonitis and the diagnosis of COVID-19 / Lakshna Mahajan, Nitin K Puri and Mukesh Kumar -- 11. Applications of machine learning in the COVID-19 pandemic : a scoping review / M.A. Jabbar, Syed Saba Raoof and Sonia Sharma.
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.
Academics, clinicians and scientists working in the domain of lung cancer, data-mining, machine learning, deep learning within the COVID-19 environment.
Also available in print.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
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. 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.
Title from PDF title page (viewed on May 8, 2022).
There are no comments for this item.