Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks
Fecha
2020Autor
Toraman, Suat
Burak Alakus, Talha
Turkoglu, Ibrahim
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Resumen
Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in
many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread
of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult.
In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis
of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning
models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection
of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection
of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multiclass, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19
disease and increase the diagnostic performance. In addition, we believe that the proposed method may
be an alternative method to diagnose COVID-19 by providing fast screening
Palabras clave
Coronavirus; Capsule networks; Deep learning; Chest x-ray images; Artificial neural networkEnlace al recurso
https://doi.org/10.1016/j.chaos.2020.110122Colecciones
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