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Deep Learning-Based Lung Infection Detection Using Radiology Modalities and Comparisons on Benchmark Datasets in COVID-19 Pandemic

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Prognostic Models in Healthcare: AI and Statistical Approaches

Part of the book series: Studies in Big Data ((SBD,volume 109))

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Abstract

The SARS-CoV-2 (severe acute respiratory syndrome coronavirus) pandemic, also known as COVID-19 (coronavirus 2019), impacted humanity worldwide and significantly impacted the healthcare community. COVID-19 infection and transmission have resulted in several international issues, including health hazards. Sore throat, trouble breathing, cough, fever, weariness, and other clinical signs have been described. In SARS-CoV-2 patients, the most common infections are in the lungs and the gastric intestine. Lung infections may be caused by viral or bacterial infections, physical trauma, or inhalation of harmful particles. This research presents deep learning-based approaches for COVID-19 infection detection based on radiological images, prevention and therapy based on benchmark publicly available datasets. Finally, the analysis and findings explore evidence-based methodologies and modalities, leading to a conclusion and possible future healthcare planning.

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Authors and Affiliations

  1. Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Jaber Alyami

  2. Imaging Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Jaber Alyami

  3. Smart Medical Imaging Research Group, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Jaber Alyami

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Editors and Affiliations

  1. Artificial Intelligence and Data Analytics Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Tanzila Saba

  2. Artificial Intelligence and Data Analytics Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Amjad Rehman

  3. Artificial Intelligence and Data Science, Jio Institute, Ulwe, Navi Mumbai, India

    Sudipta Roy

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Alyami, J. (2022). Deep Learning-Based Lung Infection Detection Using Radiology Modalities and Comparisons on Benchmark Datasets in COVID-19 Pandemic. In: Saba, T., Rehman, A., Roy, S. (eds) Prognostic Models in Healthcare: AI and Statistical Approaches. Studies in Big Data, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-19-2057-8_18

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