Smart Microscopy Camera Kit: Automatic Counting of Blood Cells in Peripheral Blood Smear Images Using RetinaNet on Raspberry Pi CM3+

Authors

  • Natthakorn Kasamsumran Chulalongkorn University
  • Amornthep Phunsin Q-Wave Systems Co., Ltd.
  • Suree Pumrin Chulalongkorn University
  • Wanchalerm Pora Chulalongkorn University

DOI:

https://doi.org/10.4186/ej.2025.29.6.43

Keywords:

microscopy imaging, peripheral blood smear, object detection, Raspberry Pi

Abstract

Microscopic examination of peripheral blood smear images for blood cell counting remains a critical yet labor-intensive task in clinical diagnostics. This research presents MicrosisDCN, an intelligent microscopy camera system designed to automate blood cell detection and counting, powered by a compact embedded platform based on the Raspberry Pi Compute Module 3+. The system incorporates a 5-megapixel image sensor and a versatile eyepiece fitting that is compatible with the most compound microscopes, providing a portable, cost-effective, and user-friendly solution. Calibration procedures ensure alignment with traditional high-power field (HPF) standards, allowing cell counts to be reported in standard mitotic count units. To detect red blood cells, white blood cells, and platelets in real-time, the system uses a special version of a deep learning model called RetinaNet, which has been improved with a technique called auto-anchor parameterization. MicrosisDCN achieves a mean Average Precision (mAP) of 86.81% in detecting a few types of blood cells with minimal errors: 1.06% for red blood cells, 0.06% for white blood cells, and 4.23% for platelets. The results indicate that MicrosisDCN, which combines traditional microscopy with advanced vision technologies, serves as an efficient, practical, and scalable solution for clinical and medical laboratory applications.

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Author Biographies

Natthakorn Kasamsumran

Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University,
Bangkok 10330, Thailand

Amornthep Phunsin

Q-Wave Systems Co., Ltd., Bangkok 10140, Thailand

Suree Pumrin

Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University,
Bangkok 10330, Thailand

Wanchalerm Pora

Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University,
Bangkok 10330, Thailand

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Published In
Vol 29 No 6, Jun 30, 2025
How to Cite
[1]
N. Kasamsumran, A. Phunsin, S. Pumrin, and W. Pora, “Smart Microscopy Camera Kit: Automatic Counting of Blood Cells in Peripheral Blood Smear Images Using RetinaNet on Raspberry Pi CM3+”, Eng. J., vol. 29, no. 6, pp. 43-58, Jun. 2025.