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

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