Multimodal Convolutional Neural Networks for Detection of Covid-19 Using Chest X-Ray and CT Images
Author:
Publisher
Allerton Press
Subject
Electrical and Electronic Engineering,General Computer Science,Electronic, Optical and Magnetic Materials
Link
https://link.springer.com/content/pdf/10.3103/S1060992X21040044.pdf
Reference22 articles.
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2. Signoroni, A., Savardi, M., Benini, S., Adami, N., Leonardi, R., Gibellini, P., Vaccher, F., Ravanelli, M., Borghesi, A., Maroldi, R., and Farina, D., BS-Net: Learning COVID-19 pneumonia severity on a large chest X‑ray dataset, Med. Image Anal., 2021, vol. 71, pp. 102046. https://doi.org/10.1016/j.media.2021.102046
3. Brunese, L., Martinelli, F., Mercaldo, F., and Santone, A., Machine learning for coronavirus covid-19 detection from chest x-rays, Procedia Comput. Sci., 2020, vol. 176, pp. 2212–2221. https://doi.org/10.1016/j.procs.2020.09.258
4. Ingacheva, A.S. and Buzmakov, A.B. Methods of preprocessing tomographic images taking into account the thermal instability of the X-ray tube, Optoelectron. Instrum. Proc., 2019, vol. 55, pp. 138–147. https://doi.org/10.3103/S8756699019020055
5. Zolotov, D.A., Asadchikov, V.E., Buzmakov, A.V., et al., X-ray diffraction tomography using laboratory sources for studying single dislocations in a low absorbing silicon single crystal, Optoelectron. Instrum. Proc., 2019, vol. 55, pp. 126–132.
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