Abstract
COVID-19 has been identified as a highly contagious and rapidly spreading disease around the world. The high infection and mortality rate characterizes this as a very dangerous disease and has been marked as a global pandemic by the world health organization. Existing COVID-19 testing methods, such as RT-PCR are not completely reliable or convenient. Since the virus affects the respiratory tract, manual analysis of chest X-rays could be a more reliable but not convenient or scalable testing technique. Hence, there is an urgent need for a faster, cheaper, and automated way of detecting the presence of the virus by automatically analyzing chest X-ray images using deep learning algorithms. ResNetV2 is one of the pre-trained deep convolutional neural network models that could be explored for this task. This paper aims to utilize the ResNetV2 model for the detection of COVID-19 from chest X-ray images to maximize the performance of this task. This study performs fine-tuning of ResNetV2 networks (specifically, ResNet101V2), which is performed in two main stages: firstly, training model with frozen ResNetV2 base layers, and secondly, unfreezing some layers of the ResNetV2 and retraining with a lower learning rate. Model fine-tuned on ResNet101V2 shows competitive and promising results with 98.50% accuracy and 97.24% sensitivity.
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Rakhymzhan, T., Zarrin, J., Maktab-Dar-Oghaz, M., Saheer, L.B. (2022). Deep Convolutional Neural Networks for COVID-19 Detection from Chest X-Ray Images Using ResNetV2. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_8
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