A New Few-Shot Learning Method of Bacterial Colony Counting Based on the Edge Computing Device

Author:

Zhang BeiniORCID,Zhou Zhentao,Cao Wenbin,Qi Xirui,Xu Chen,Wen Weijia

Abstract

Bacterial colony counting is a time consuming but important task for many fields, such as food quality testing and pathogen detection, which own the high demand for accurate on-site testing. However, bacterial colonies are often overlapped, adherent with each other, and difficult to precisely process by traditional algorithms. The development of deep learning has brought new possibilities for bacterial colony counting, but deep learning networks usually require a large amount of training data and highly configured test equipment. The culture and annotation time of bacteria are costly, and professional deep learning workstations are too expensive and large to meet portable requirements. To solve these problems, we propose a lightweight improved YOLOv3 network based on the few-shot learning strategy, which is able to accomplish high detection accuracy with only five raw images and be deployed on a low-cost edge device. Compared with the traditional methods, our method improved the average accuracy from 64.3% to 97.4% and decreased the False Negative Rate from 32.1% to 1.5%. Our method could greatly improve the detection accuracy, realize the portability for on-site testing, and significantly save the cost of data collection and annotation over 80%, which brings more potential for bacterial colony counting.

Funder

2019 Shenzhen-Hong Kong Innovation Circle (Category D)

Publisher

MDPI AG

Subject

General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting;Microorganisms;2024-01-18

2. Automated Bacterial Colony Counting on Agar Plate;2023 15th Biomedical Engineering International Conference (BMEiCON);2023-10-28

3. ML Classification Methods Comparison for Breast Cancer Diagnosis in Clinical Application Field;Highlights in Science, Engineering and Technology;2023-03-30

4. A Technical Comparison of YOLO-Based Chest Cancer Diagnosis Methods;Highlights in Science, Engineering and Technology;2023-03-30

5. On Potentials of Few-Shot Learning for AI-Enabled Internet of Medical Things;2022 IEEE Globecom Workshops (GC Wkshps);2022-12-04

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