Abstract
Recently, diagnosis of COVID-19 has become an urgent worldwide concern. One modality for disease diagnosis that has not yet been well explored is that of X-ray images. To explore the possibility of automated COVID-19 diagnosis from X-ray images, we use deep CNNs based on ResNet-18 and InceptionResNetV2 to classify X-ray images from patients under three conditions: normal, COVID-19, and other pneumonia. Experimental results show that deep CNNs can distinguish normal patients from diseased patients with accuracy 93.41%, and among diseased patients, it can distinguish COVID-19 from other pneumonia cases with accuracy 93.53%. The trained model is able to uncover the detailed appearance features that distinguish COVID-19 infections from other pneumonia.
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Kunapinun, A., Dailey, M.N. (2022). COVID-19 X-ray Image Diagnosis Using Deep Convolutional Neural Networks. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_64
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DOI: https://doi.org/10.1007/978-981-16-2380-6_64
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