ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis

Binod Bhattarai; Eduard Vazquez; Sandesh Pokhrel; Sanjay Bhandari; Yash Raj Shrestha

ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis

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arxiv 2310.04749 v1 pith:ESNXFZCZ submitted 2023-10-07 cs.CV cs.AI

Sandesh Pokhrel , Sanjay Bhandari , Eduard Vazquez , Yash Raj Shrestha , Binod Bhattarai This is my paper

classification cs.CVcs.AI

keywords coronaryarterydiagnosislesionsmaskmodelprocessstenotic

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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.

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