The Role of Computer Vision and Deep Learning Algorithms in Medical Imaging Analysis and Automated Disease Detection Systems

Abstract

Computer vision and deep learning algorithms have revolutionized medical imaging analysis and automated disease detection systems by enabling high-precision image interpretation and early diagnosis. This study evaluates the effectiveness of these technologies in improving diagnostic accuracy, reducing human error, and enhancing clinical workflows. A convergent mixed-methods approach was employed, combining quantitative data from 172 healthcare professionals and imaging specialists with qualitative insights from clinical case studies and expert interviews. The findings indicate that deep learning-based systems significantly improve diagnostic performance, particularly in radiology and pathology, while reducing analysis time and variability. However, challenges such as data quality, algorithm transparency, and integration with clinical workflows remain significant. The study provides recommendations for optimizing the use of computer vision technologies in healthcare.