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  • [\'Electrical Engineering, Federal University of Piauí - UFPI, Teresina, PI, Brazil. Electronic address: edsondamasceno@ufpi.edu.br.\', \'Electrical Engineering, Federal University of Piauí - UFPI, Teresina, PI, Brazil; Information Systems, Federal University of Piauí - UFPI, Picos, PI, Brazil. Electronic address: romuere@ufpi.edu.br.\', \'Electrical Engineering, Federal University of Piauí - UFPI, Teresina, PI, Brazil; Information Systems, Federal University of Piauí - UFPI, Picos, PI, Brazil. Electronic address: flavio86@ufpi.edu.br.\', \'Electrical Engineering, Federal University of Piauí - UFPI, Teresina, PI, Brazil; Computer Science, Federal University of Piauí - UFPI, Teresina, PI, Brazil. Electronic address: ricardoalr@ufpi.edu.br.\', \'Electrical Engineering, Federal University of Piauí - UFPI, Teresina, PI, Brazil; Information Systems, Federal University of Piauí - UFPI, Picos, PI, Brazil. Electronic address: antoniooseas@ufpi.edu.br.\']
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  • S0010-4825(21)00538-210.1016/j.compbiomed.2021.104744
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  • Computers in biology and medicine
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  • 34388465
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  • An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms.
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  • 2021

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