Bacterial colony counting by Convolutional Neural Networks.
Annu Int Conf IEEE Eng Med Biol Soc
; 2015: 7458-61, 2015.
Article
em En
| MEDLINE
| ID: mdl-26738016
ABSTRACT
Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Contagem de Colônia Microbiana
/
Redes Neurais de Computação
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article