Abstract
COVID-19 created a history in the world of medicine which leads to more usage of technologies such as deep-learning models to aid in the early detection of COVID-19 using medical imaging from three commonly used modalities: X-Ray, Ultrasound and Computerized Tomography (CT) scan. This research aims to provide medical professionals with an additional tool to assist in devising an appropriate treatment plan and making disease containment decisions. We have identified the suitable optimized VGG19 and MobNetCov19 architecture through a Convolutional Neural Network (CNN) model for a comparative study of the different imaging modes to develop highly curated COVID-19 detection models despite the scarcity of COVID-19 datasets. Our results demonstrate that CT dataset has the highest detection accuracy compared to X-Ray and Ultrasound datas. Although the limited data made training complex models challenging, the selected MobNetCov19 model, extensively tuned with appropriate parameters, performed considerably well up to 100%, 98%, and 98% of accuracy for CT, X-Ray, and Ultra sound respectively.
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Suresh Kumar, H.S., Bhoomika, S., Pushpa, C.N., Thriveni, J., Venugopal, K.R. (2024). MobNetCov19: Detection of COVID-19 Using MobileNetV2 Architecture for Multi-mode Images. In: Aurelia, S., J., C., Immanuel, A., Mani, J., Padmanabha, V. (eds) Computational Sciences and Sustainable Technologies. ICCSST 2023. Communications in Computer and Information Science, vol 1973. Springer, Cham. https://doi.org/10.1007/978-3-031-50993-3_36
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DOI: https://doi.org/10.1007/978-3-031-50993-3_36
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