A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 10 2023
31 10 2023
Historique:
received:
08
05
2023
accepted:
05
10
2023
medline:
2
11
2023
pubmed:
1
11
2023
entrez:
1
11
2023
Statut:
epublish
Résumé
We present a new approach to segment and classify bacterial spore layers from Transmission Electron Microscopy (TEM) images using a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) classifier algorithm. This approach utilizes deep learning, with the CNN extracting features from images, and the RF classifier using those features for classification. The proposed model achieved 73% accuracy, 64% precision, 46% sensitivity, and 47% F1-score with test data. Compared to other classifiers such as AdaBoost, XGBoost, and SVM, our proposed model demonstrates greater robustness and higher generalization ability for non-linear segmentation. Our model is also able to identify spores with a damaged core as verified using TEMs of chemically exposed spores. Therefore, the proposed method will be valuable for identifying and characterizing spore features in TEM images, reducing labor-intensive work as well as human bias.
Identifiants
pubmed: 37907463
doi: 10.1038/s41598-023-44212-5
pii: 10.1038/s41598-023-44212-5
pmc: PMC10618482
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
18758Informations de copyright
© 2023. The Author(s).
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