Object Detection for Automated Coronary Artery Using Deep Learning
Hadis Keshavarz Hossein Sadr
In the era of digital medicine, medical imaging serves as a widespread
technique for early disease detection, with a substantial volume of images
being generated and stored daily in electronic patient records. X-ray
angiography imaging is a standard and one of the most common methods for
rapidly diagnosing coronary artery diseases. The notable achievements of recent
deep learning algorithms align with the increased use of electronic health
records and diagnostic imaging. Deep neural networks, leveraging abundant data,
advanced algorithms, and powerful computational capabilities, prove highly
effective in the analysis and interpretation of images. In this context, Object
detection methods have become a promising approach, particularly through
convolutional neural networks (CNN), streamlining medical image analysis by
eliminating manual feature extraction. This allows for direct feature
extraction from images, ensuring high accuracy in results. Therefore, in our
paper, we utilized the object detection method on X-ray angiography images to
precisely identify the location of coronary artery stenosis. As a result, this
model enables automatic and real-time detection of stenosis locations,
assisting in the crucial and sensitive decision-making process for healthcare
professionals.