Analysis and modeling of digital solutions in medical database management

¹ Urgench branch of Tashkent Medical Academy, Urgench, Uzbekistan
² Tashkent University of Information Technologies named after Muhammad al Khwarizmi, Tashkent, Uzbekistan

^* Corresponding author: bahtiyar1975@mail.ru

Abstract

This paper explores the integration of computer vision techniques within medical database management systems, focusing on their potential to enhance data accuracy and operational efficiency in healthcare. The study analyzes current methodologies and proposes enhancements based on recent technological advancements, particularly in graphics processor architectures. The research employs a combination of theoretical analysis and computational modeling, with a detailed examination of the G80 graphics processor architecture. The Viola-Jones method is utilized as a model algorithm for object detection in medical images, implemented using the OpenCV library. The study evaluates the algorithm's performance on 1024x1024 pixel images, varying parameters such as sliding window size, scaling factor, and number of classifiers. A novel method for locating key areas in medical images using Haar-like and contour features is proposed and evaluated on a dataset of MRI images. The results demonstrate superior performance compared to reference techniques, maintaining over 90% accuracy in locating key areas even with a database of 200 images. This high accuracy is attributed to expanded Haar-like feature templates, efficient computation using integral images, and a comprehensive contour feature extraction process. The paper concludes that the proposed method shows promise for enhancing medical image analysis and computer-aided diagnosis.