Application of Computer Vision for Automated Processing of Medical Documents

Abstract

This paper examines the automation of medical image processing for diagnosing arterial hypertension using artificial intelligence and computer vision technologies. A software component has been developed that automatically extracts and structures information from visual representations of medical documents (including biochemical analysis results, complete blood counts, and 24-hour blood pressure monitoring data), minimizing errors and accelerating the process of entering and interpreting medical information. Algorithms for image preprocessing (increasing image resolution, noise removal, and tilt correction), segmentation, and text recognition were developed and tested using the Real-ESRGAN and EasyOCR neural network models. Particular attention was paid to improving text recognition quality in the presence of characteristic artifacts that arise when scanning or photographing documents. CER and WER metrics were used to evaluate quality, and the module's performance was assessed with and without superresolution. The results of the study confirmed the effectiveness of the proposed approach and demonstrated that the integration of Real-ESRGAN technology improves the accuracy of medical image processing in the presence of significant noise and low-resolution source data. The practical significance of the study lies in simplifying and accelerating the process of diagnosing hypertension and creating the basis for a personalized approach to patient treatment.