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A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-0168-0197
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-0496-6692
Umeå University, Faculty of Science and Technology, Department of Physics. Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). (The Biophysics and Biophotonics group)ORCID iD: 0000-0002-9835-3263
2023 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 18758Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature, 2023. Vol. 13, no 1, article id 18758
National Category
Other Physics Topics Other Computer and Information Science
Identifiers
URN: urn:nbn:se:umu:diva-216165DOI: 10.1038/s41598-023-44212-5PubMedID: 37907463Scopus ID: 2-s2.0-85175591485OAI: oai:DiVA.org:umu-216165DiVA, id: diva2:1809565
Funder
Swedish Research Council, 2019-04016The Kempe Foundations, JCK-2129.3Available from: 2023-11-04 Created: 2023-11-04 Last updated: 2023-12-01Bibliographically approved

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Qamar, SaqibÖberg, RasmusMalyshev, DmitryAndersson, Magnus

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