We (Medical Imaging Group (MIG), CDS, IISc Bangalore and ACPS Group, ICT, UiA Campus Grimstad) have developed a Mobile-friendly deep learning model for point-of-care detection of COVID19 using Ultrasound Images for better triaging of patients. This manuscript is accepted for publication in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (Special issue on Ultrasound in COVID-19 and Lung Diagnostics).
Navchetan Awasthi, Aveen Dayal, Linga R. Cenkeramaddi, and Phaneendra K. Yalavarthy, “Mini-COVIDNet : Efficient Light Weight Deep Neural Network for Ultrasound based Point-of-Care Detection of COVID-19,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (Special issue on Ultrasound in COVID-19 and Lung Diagnostics) 2021 (in press). [Reprint is available at: http://cds.iisc.ac.in/faculty/yalavarthy/Publications.html]
Project Repository: https://github.com/navchetan-awasthi/Mini-COVIDNet
Keywords: COVID-19, Lung, Ultrasonic imaging, Computed, tomography, Imaging, Diseases, X-ray imaging
Abstract: Lung ultrasound imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we developed a lightweight mobile-friendly efficient deep learning model for the detection of COVID-19 using lung ultrasound images. The developed method was shown to be sensitive to the damage to the pleural surface of the lung, which has been proven to have prognostic value, commonly observed in intensive care unit–admitted and deceased patients. The developed model has utility in the context of a massive COVID-19 pandemic, where it can better triage patients with pulmonary symptoms (suspected of infection).
More details:DOI: 10.1109/TUFFC.2021.3068190