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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.NCET009
Volume:
10 |
10 |
Oct 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
BSC Information Technology
Thassim Beevi Abdul Kader College for Women
Clinical microbiology laboratories rely on manual bacterial colony counting and morphological identification, which are time-consuming and prone to human error. This project presents Microbe Lens, a deep learning-based framework designed to automate bacterial image analysis and laboratory report generation. The proposed system utilizes the YOLOv8 object detection model trained on publicly available biomedical image datasets to detect, classify, and count bacterial colonies, including overlapping colonies. It also identifies bacterial characteristics such as Gram status and cellular morphology. The detection module is integrated with a FastAPI backend that processes microbiological images and combines image-based analysis with patient clinical information to support diagnostic decision-making. The analysed results are automatically converted into a standardized PDF laboratory report using the Report Lab library, reducing manual documentation and reporting time. The novelty of Microbe-Lens lies in analysis with automated laboratory report generation in a single end-to-end framework, providing an efficient solution for clinical microbiology laboratories.
KEYWORDS
This paper presented Microbe-Lens, an end-to-end automated framework designed to streamline clinical microbiology workflows by combining custom YOLOv8-based bacterial colony detection with a FastAPI and ReportLabdriven automated reporting system. Future work will focus on expanding the model to identify a broader spectrum of pathogenic microorganisms and enhancing deployment in resourceconstrained laboratory environments.
[1] M. Talo, "An Automated Deep Learning Approach for Bacterial Image Classification," International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES), pp. 26-28, 2019. [2] M. Amano et al., "Deep Learning Approach for Classifying Bacteria types using Morphology of Bacterial Colony," IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2165-2168, 2022. [3] M. F. Amri et al., "Bacterial Classification Using Deep Structured Convolutional Neural Network for Low Resource Data," Jurnal Elektronika dan Telekomunikasi (JET), vol. 23, no. 1, pp. 47-54, 2023. [4] K. G. Mannava, M. R. Kalyan, and K. S. S. Praneeth, "Bacterial Image Classification Using Deep Learning Approach," Journal of Emerging Technologies and Innovative Research (JETIR), vol. 11, no. 4, pp. g379-g386, 2024. [5] S. A. Nagro et al., "Automatic Identification of Single Bacterial Colonies Using Deep and Transfer Learning," IEEE Access, vol. 10, pp. 120181-120190, 2022. [6] Ultralytics, "YOLOv8: State-of-the-art YOLO models," GitHub repository, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics [7] Tiangolo, "FastAPI: High performance, easy to learn, fast to code, ready for production," 2018. [Online]. Available: https://github.com/tiangolo/fastapi [8] The ReportLab Open Source PDF Library User Guide, ReportLab Inc., 2023.
Publication Date
10/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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