  Coronary Artery Diseases although preventable are one of the leading cause of
mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has
proved challenging. This study addresses the automation of resource-intensive
and time-consuming process of manually detecting stenotic lesions in coronary
arteries in X-ray coronary angiography images. To overcome this challenge, we
employ a specialized Convnext-V2 backbone based Mask RCNN model pre-trained for
instance segmentation tasks. Our empirical findings affirm that the proposed
model exhibits commendable performance in identifying stenotic lesions.
Notably, our approach achieves a substantial F1 score of 0.5353 in this
demanding task, underscoring its effectiveness in streamlining this intensive
process.
