Reducing the Counting Time of Colonies Using Image Processing Techniques

Microbial colony counting is a crucial process in microbiology laboratories and medical research, yet it is often time-consuming and labor-intensive, particularly in large-scale settings or when dealing with numerous samples. Incorporating image processing techniques can significantly reduce the tim...

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Bibliographic Details
Main Authors: Siangchin, Apichai, Ploysuwan, Tuchsanai
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
colony counting
deep learning
Escherichia coli
Image processing
Laboratories
Manuals
Personnel
Process control
Streaming media
Training
YOLO
Online Access:Request full text
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Summary:Microbial colony counting is a crucial process in microbiology laboratories and medical research, yet it is often time-consuming and labor-intensive, particularly in large-scale settings or when dealing with numerous samples. Incorporating image processing techniques can significantly reduce the time and error associated with manual colony counting. In this study, we developed and evaluated a deep learning model for automated E. coli colony counting using the YOLO (You Only Look Once) framework. The model achieved an mAP50 of 90.3%, demonstrating its high accuracy and potential for real-world application in laboratories of various sizes. This research not only streamlines the colony counting process but also allows laboratory personnel to allocate their time to other essential tasks.
ISSN:2642-3901
DOI:10.23919/ICCAS63016.2024.10773099