Automated Bacteria Colony Counting using Hybrid Image Segmentation Algorithm and YOLOv5 Transfer Learning Model

Authors: Reznee Mariel R. Galope, Cherry B. Lisondra, Audrey Carmel Jay J. Nanual

DOI: https://doi.org/10.37082/IJIRMPS.IPMESS-24.6

Short DOI: https://doi.org/mgfb

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Abstract: Bacteria colony counting is a critical process in microbiology research, but manual colony counting remains tedious and error-prone, motivating the need for automation. This study aimed to develop an automated system for accurately detecting and counting E. coli colonies on agar plates. The research objectives were achieved by generating a dataset of E. coli colony images, developing a hybrid image segmentation algorithm, and training a YOLOv5 transfer learning model. The dataset was created by capturing images of E. coli colonies on agar plates under controlled conditions. The cultured agar plates were generated by Davao Oriental State University (DOrSU). A novel framework for a hybrid image segmentation algorithm combining Watershed and Falling-Ball was developed to address the challenge of accurately segmenting colonies from complex backgrounds. The algorithm utilized the output of the Watershed Algorithm to create a binary mask, which the Falling-Ball Algorithm further refined to improve edge detection and fill gaps. The YOLOv5 transfer learning model was trained using the generated dataset to detect and count E. coli colonies. The model achieved a detection accuracy of up to 75%, providing a reliable automated solution for colony counting. Performance evaluation metrics such as precision, recall, and mAP_0.5 were utilized to assess the model's performance. However, training the model using the dataset that underwent the framework could not proceed due to its resource-intensive requirements.

Keywords: Automated Bacteria Colony Counting, Escherichia coli, E. coli, Image Segmentation, Novel Framework, hybrid algorithm, Watershed, Falling-Ball, YOLOv5, Transfer Learning

Paper Id: 4.206

Published On: 2024-01-30

Published In: Special Issue - International Conference on Innovative Practices in Management, Engineering & Social Sciences (January 2024)

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