AI-driven convolutional neural networks for accurate identification of yellow fever vectors.
Parasit Vectors
; 17(1): 329, 2024 Aug 02.
Article
em En
| MEDLINE
| ID: mdl-39095920
ABSTRACT
BACKGROUND:
Identifying mosquito vectors is crucial for controlling diseases. Automated identification studies using the convolutional neural network (CNN) have been conducted for some urban mosquito vectors but not yet for sylvatic mosquito vectors that transmit the yellow fever. We evaluated the ability of the AlexNet CNN to identify four mosquito species Aedes serratus, Aedes scapularis, Haemagogus leucocelaenus and Sabethes albiprivus and whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions.METHODS:
The specimens were photographed using a cell phone connected to a stereoscope. Photographs were taken of the full-body, pronotum and lateral view of the thorax, which were pre-processed to train the AlexNet algorithm. The evaluation was based on the confusion matrix, the accuracy (ten pseudo-replicates) and the confidence interval for each experiment.RESULTS:
Our study found that the AlexNet can accurately identify mosquito pictures of the genus Aedes, Sabethes and Haemagogus with over 90% accuracy. Furthermore, the algorithm performance did not change according to the body regions submitted. It is worth noting that the state of preservation of the mosquitoes, which were often damaged, may have affected the network's ability to differentiate between these species and thus accuracy rates could have been even higher.CONCLUSIONS:
Our results support the idea of applying CNNs for artificial intelligence (AI)-driven identification of mosquito vectors of tropical diseases. This approach can potentially be used in the surveillance of yellow fever vectors by health services and the population as well.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Febre Amarela
/
Redes Neurais de Computação
/
Aedes
/
Mosquitos Vetores
Limite:
Animals
Idioma:
En
Revista:
Parasit Vectors
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Reino Unido