鉴定(生物学)
人工智能
计算机科学
抗真菌
机器学习
生物
生态学
微生物学
作者
Maria Carolina Accioly Brelaz de Castro,Leandro Almeida,Renan Williams M. Ferreira,Clayton A. Benevides,Cleber Zanchettin,Frederico Duarte de Menezes,Cícero Pinheiro Inácio,Reginlado G. de Lima-Neto,José Gilson A. T. Filho,Rejane Pereira Neves
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-07-01
卷期号:22 (13): 12493-12503
被引量:2
标识
DOI:10.1109/jsen.2022.3178346
摘要
Infections triggered by fungi of the genus Candida are widely known, although the high incidence and mortality factors are still unclear. The classic methods of identifying Candida species are subject to errors, requiring new techniques with faster and more accurate performance. We present a study for identifying fungi species by analyzing volatile organic compounds of cultures acquired and interpreted using Electronic Nose and Artificial Intelligence methods. The proposed approach contributes to establishing an agile and appropriate treatment, reducing the complications of the disease and the number of deaths. We perform experiments with three species of Candida obtaining accuracy above 90% in the fungi identification. Therefore, future works are encouraged to deal with more types of fungi to help create a new identification methodology faster and more reliable using artificial intelligence methods.
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