Please use this identifier to cite or link to this item:
http://repositorio.ufla.br/jspui/handle/1/46623| Title: | CT-FastNet: Detecção de COVID-19 a partir de Tomografias Computadorizadas (TC) de Tórax usando Inteligência Artificial |
| Other Titles: | CT-FastNet: Detection of COVID-19 From Chest Computed Tomography (CT) Images Using Artificial Intelligence |
| Keywords: | COVID-19 Redes neurais artificiais Tomografia computadorizada Artificial neural networks Computed tomography |
| Issue Date: | Jul-2020 |
| Publisher: | Brazilian Journals Publicações de Periódicos e Editora Ltda. |
| Citation: | BARBOSA, R. C. et al. CT-FastNet: Detecção de COVID-19 a partir de Tomografias Computadorizadas (TC) de Tórax usando Inteligência Artificial. Brazilian Journal of Development, Curitiba, v. 6, n. 7, p. 50315-50330, jul. 2020. DOI:10.34117/bjdv6n7-619. |
| Abstract: | Many countries have been affected by the COVID-19, and health departments are facing delays to detect the new coronavirus symptoms. Artificial Intelligence (AI) models are designed for the automatic detection of respiratory diseases patterns using computed tomography (CT) scans of the chest. However, the training time consumed by the algorithms is a key parameter that is not properly attended. In this article, we propose an AI solution using an activation function that helps to obtain a low training time. Experimental results show that our proposal overcome several deep learning architectures, such as the 3D deep Convolutional Neural Network to Detect COVID-19 (DeCoVNet). |
| URI: | https://doi.org/10.34117/bjdv6n7-619 http://repositorio.ufla.br/jspui/handle/1/46623 |
| Appears in Collections: | DCC - Artigos publicados em periódicos |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
Admin Tools