Abstract
Due to the outbreak of corona virus disease (COVID-19) globally, many countries are facing shortages of testing kits and medical resources. Moreover, the current COVID-19 swab test cannot easily perform due to asymptomatic patients. To assist the medical staff, few studies have proposed to detect and classify COVID-19 cases by analyzing radiological images. In this paper, we aim to develop an alternative method using chest X-ray images to provide an automatic and faster diagnosis. Convolutional neural network models that can detect the presence of COVID-19 and pneumonia infection from chest X-ray images are developed by exploiting transfer learning techniques. Three models were developed for comparison, the models yielded an accuracy of 97.3%, 98.2%, and 97.3% respectively.
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Thean, S.K.J., Nafea, M., Nugroho, H. (2022). Development of COVID-19 Prediction Models from Chest X-Ray Using Transfer Learning. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_72
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DOI: https://doi.org/10.1007/978-981-16-8129-5_72
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