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COVID-19 Project publication
Title of the paper: COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
M. J. Horry, S. Chakraborty, M. Paul, A. Ulhaq, B. Pradhan, M. Saha, and N. Shukla (2020), “COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data,” IEEE Access, pp. 149808 – 149824, 2020. DOI: 10.1109/ACCESS.2020.3016780
Early COVID-19 detection helps in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. The proposed model performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.
Access the full text of the paper from the following IEEE website: https://ieeexplore.ieee.org/document/9167243