U
ResNet50
U2-Net
bacterial colony counting
convolutional neural networks
image segmentation
image spatial normalization
light intensity correction
Journal
Microorganisms
ISSN: 2076-2607
Titre abrégé: Microorganisms
Pays: Switzerland
ID NLM: 101625893
Informations de publication
Date de publication:
18 Jan 2024
18 Jan 2024
Historique:
received:
18
12
2023
revised:
15
01
2024
accepted:
16
01
2024
medline:
23
1
2024
pubmed:
23
1
2024
entrez:
23
1
2024
Statut:
epublish
Résumé
In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation between colony and media areas. Thirdly, a U
Identifiants
pubmed: 38258027
pii: microorganisms12010201
doi: 10.3390/microorganisms12010201
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : National Natural Science Foundation of China
ID : 82202531