An identification system for blood pressure values powered by deep learning

Tsai-Rong Chang; Kuo-Chen Lee; Xiang-Ming Fu

doi:10.1117/12.3058014

5 February 2025 An identification system for blood pressure values powered by deep learning

Tsai-Rong Chang, Kuo-Chen Lee, Xiang-Ming Fu

Author Affiliations +

Proceedings Volume 13510, International Workshop on Advanced Imaging Technology (IWAIT) 2025; 135101E (2025) https://doi.org/10.1117/12.3058014
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2025, 2025, Douliu City, Taiwan

Abstract

This study presents an innovative application utilizing image recognition technology to automatically identify values displayed on electronic blood pressure monitors. This study uses deep learning, specifically a Mask-RCNN-based algorithm, to accurately detect and recognize numerical readings on monitor panels. By incorporating advanced image processing techniques such as mask binarization, Canny edge detection, and Hough transform, the system corrects image distortions caused by varying camera angles, transforming them into standardized rectangular formats for precise recognition. Rigorous testing under diverse lighting and angle conditions ensured consistent high-precision results. This solution demonstrates significant potential for automating medical data management through image recognition technology.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tsai-Rong Chang, Kuo-Chen Lee, and Xiang-Ming Fu "An identification system for blood pressure values powered by deep learning", Proc. SPIE 13510, International Workshop on Advanced Imaging Technology (IWAIT) 2025, 135101E (5 February 2025); https://doi.org/10.1117/12.3058014

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