Deep Learning for Agar Plate Analysis: Predicting Microbial Cluster Counts

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but.event.date23.04.2024cs
but.event.titleSTUDENT EEICT 2024cs
dc.contributor.authorČičatka, Michal
dc.contributor.authorBurget, Radim
dc.date.accessioned2024-07-09T07:38:38Z
dc.date.available2024-07-09T07:38:38Z
dc.date.issued2024cs
dc.description.abstractManual analysis of agar plates remains a bottleneck in microbiology, hindering automation efforts. This study investigates the feasibility of using machine learning for automated microbial cluster count detection from agar plate images. We employed various methods, including elbow detection (baseline) and supervised learning models (Support Vector Regression, Simple CNN, XGBoost, Random Forest, pre-trained VGG, and pre-trained Inceptionv3). The results demonstrate that machine learning models significantly outperform the baseline, achieving lower prediction errors and higher accuracy in identifying the correct number of clusters. Notably, both pre-trained VGG and InceptionV3 achieved strong performance, highlighting the effectiveness of transfer learning for this task. InceptionV3 exhibited the lowest error rates overall. This study establishes a foundation for developing robust automated systems for quantifying microbial growth, potentially streamlining workflows and improving efficiency in microbiological research and clinical settings.en
dc.formattextcs
dc.format.extent197-201cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings I of the 30st Conference STUDENT EEICT 2024: General papers. s. 197-201. ISBN 978-80-214-6231-1cs
dc.identifier.isbn978-80-214-6231-1
dc.identifier.issn2788-1334
dc.identifier.urihttps://hdl.handle.net/11012/249233
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 30st Conference STUDENT EEICT 2024: General papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectimage processingen
dc.subjectmachine learningen
dc.subjectagar platesen
dc.titleDeep Learning for Agar Plate Analysis: Predicting Microbial Cluster Countsen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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