Abstract
Mass testing of the population is among the most effective measures to combat the COVID-19 pandemic. Among existing diagnostic methods, deep learning-based solutions have the potential to be affordable, quick and accurate. However, these techniques often rely on high-quality datasets, which are not always available in medical scenarios. In this work, we use convolutional neural networks to diagnose COVID-19 on computed tomography images from the COVIDx-CT dataset [6]. The available scans often present noisy artifacts, originated from sensor- and capturing-related errors, that can negatively impact the performance of the model if left untreated. In this sense, we explore several preprocessing strategies to reduce their impact and obtain a more accurate method. Our best model, a ResNet50 fine-tuned with preprocessed images, obtained \(97.84\%\) accuracy when prompted with a single image and \(99.50\%\) when processing multiple images from the same patient. In addition to achieving high accuracy, interpretability experiments show that the network correctly learned features from the lung and chest area.
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Ferber, D. et al. (2021). Deep Learning-Based COVID-19 Diagnostics of Low-Quality CT Images. In: Stadler, P.F., Walter, M.E.M.T., Hernandez-Rosales, M., Brigido, M.M. (eds) Advances in Bioinformatics and Computational Biology. BSB 2021. Lecture Notes in Computer Science(), vol 13063. Springer, Cham. https://doi.org/10.1007/978-3-030-91814-9_7
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DOI: https://doi.org/10.1007/978-3-030-91814-9_7
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