An identification system for blood pressure values powered by deep learning

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.