Automatic classification of cells in microscopic fecal images using convolutional neural networks.
Biosci Rep
; 39(4)2019 04 30.
Article
em En
| MEDLINE
| ID: mdl-30872411
ABSTRACT
The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Contagem de Colônia Microbiana
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Contagem de Eritrócitos
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Fezes
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Contagem de Leucócitos
Limite:
Humans
Idioma:
En
Revista:
Biosci Rep
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
China