Reducing the Counting Time of Colonies Using Image Processing Techniques

Simple item page
dc.contributor.authorSiangchin, Apichai
dc.contributor.authorPloysuwan, Tuchsanai
dc.date.accessioned2026-08-06T10:43:25Z
dc.date.available2026-08-06T10:43:25Z
dc.date.issued2024-01-01
dc.description.abstractMicrobial 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.
dc.identifier.citationInternational Conference on Control Automation and Systems, 1276-1280, 2024
dc.identifier.doi10.23919/ICCAS63016.2024.10773099
dc.identifier.issn15987833
dc.identifier.other2-s2.0-85214409912
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14931
dc.sourceInternational Conference on Control Automation and Systems
dc.subjectcolony counting
dc.subjectdeep learning
dc.subjectimage processing
dc.subjectYOLO
dc.titleReducing the Counting Time of Colonies Using Image Processing Techniques
dc.typeConference Paper

Files

Collections

Research