LRSE-Net: Lightweight Residual Squeeze-and-Excitation Network for Stenosis Detection in X-ray Coronary Angiography

Abstract

Coronary heart disease is the primary cause of death worldwide. Among these, ischemic heart disease and stroke are the most common diseases induced by coronary stenosis. This study presents a Lightweight Residual Squeeze-and-Excitation Network (LRSE-Net) for stenosis classification in X-ray Coronary Angiography images. The proposed model employs redundant kernel deletion and tensor decomposition by Depthwise Separable Convolutions to reduce the model parameters up to 48.6 x concerning a Vanilla Residual Squeeze-and-Excitation Network. Furthermore, the reduction ratios of each Squeeze-and-Excitation module are optimized individually to improve the feature recalibration. Experimental results for Stenosis Detection on the publicly available Deep Stenosis Detection Dataset and Angiographic Dataset demonstrate that the proposed LRSE-Net achieves the best Accuracy—0.9549/0.9543, Sensitivity—0.6320/0.8792, Precision—0.5991/0.8944, and F1-score—0.6103/0.8944, as well as competitive Specificity of 0.9620/0.9733.