An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis

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Abstract

As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user-friendly platforms for clinicians and researchers in an integrated bioinformatics approach. The diagnosis and treatment of COVID-19 disease can be accelerated using AI-based platforms. In the battle against the virus, however, researchers and decision-makers must contend with an ever-increasing volume of data, referred to as "big data."VGG19 and ResNet152V2 pretrained deep learning architectures were used in this study. With these datasets, we could train and fine-tune our model on lung ultrasound frames from healthy people as well as from patients with COVID-19 and pneumonia. In two separate experiments, we evaluated two different classes of predictive models: one against pneumonia and the other against non-COVID-19. COVID-19 can be detected and diagnosed accurately and efficiently using these models, according to the findings. Therefore, the use of these inexpensive and affordable deep learning methods should be considered as a reliable method for the diagnosis of COVID-19.

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Sangeetha, S. K. B., Kumar, M. S., Deeba, K., Rajadurai, H., Maheshwari, V., & Dalu, G. T. (2022). An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis. Computational and Mathematical Methods in Medicine, 2022. https://doi.org/10.1155/2022/9771212

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