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Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Toraman, Suat ; Alakus, Talha Burak ; Turkoglu, Ibrahim

Chaos, solitons and fractals, 2020-11, Vol.140, p.110122-110122, Article 110122 [Periódico revisado por pares]

England: Elsevier Ltd

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  • Título:
    Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks
  • Autor: Toraman, Suat ; Alakus, Talha Burak ; Turkoglu, Ibrahim
  • Assuntos: Artificial neural network ; Capsule networks ; Chest x-ray images ; Coronavirus ; Deep learning
  • É parte de: Chaos, solitons and fractals, 2020-11, Vol.140, p.110122-110122, Article 110122
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
  • Descrição: •In this study, unlike CNN architectures, COVID-19 was determined from chest X-ray images with a smaller number of layers.•More COVID-19, pneumonia, and no-findings images were used than in previous studies. This increases the reliability of the system more.•As is known, reducing the size of the image may cause some information in the image to be lost. Given these facts, good classification accuracy has been achieved with capsule networks, even the image size has been reduced to 128 × 128 pixels. 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 multi-class, 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.
  • Editor: England: Elsevier Ltd
  • Idioma: Inglês
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