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Paper ID:10.55041/IJSREM.NCET009

MICROBE-LENS A Deep Learning Framework for Automated Bacterial Image Analysis and Laboratory Report Generation

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM.NCET009

Volume:

10 |

10 |

Oct 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem.NCET009

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.

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Authors

ZUA

Z. Ummul Aamina

BSC Information Technology

Thassim Beevi Abdul Kader College for Women

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Abstract

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

Deep Learning YOLOv8 Computer Vision Medical Image Analysis Clinical Microbiology FastAPI Automated Laboratory Report Generation.

Cite This Article

Z. Ummul Aamina, "MICROBE-LENS A Deep Learning Framework for Automated Bacterial Image Analysis and Laboratory Report Generation," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-6, Oct 2026. https://doi.org/10.55041/ijsrem.NCET009

Full Article/Conclusion

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.

References

[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.

Editorial & Review Transparency

Publication Date

10/5/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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2

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