Abstract
Catering to the widespread COVID-19 pandemic, the authors aim to develop a system based on machine learning combined with the knowledge of medical science. Considering the prevailing situation, it becomes necessary to diagnose the COVID-19 at initial stages. The idea behind the described designed model is to identify the spread of infection in patients as fast as possible. The paper sketches two different approaches: K-fold cross-validation and deep network designer which are based on deep learning technology for the prediction of COVID-19 in the initial stages by using the chest X-rays. The performance evaluation of the cross-fold validation process is compared with the designed application in the deep network designer to find an effective and efficient methodology for classification which attained better accuracy.
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Patel, M., Padiya, J., Patel, M.I. (2023). Deep Learning-Based COVID-19 Detection Using Transfer Learning Through ResNet-50. In: Dhavse, R., Kumar, V., Monteleone, S. (eds) Emerging Technology Trends in Electronics, Communication and Networking. Lecture Notes in Electrical Engineering, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-19-6737-5_21
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DOI: https://doi.org/10.1007/978-981-19-6737-5_21
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