A New Coronary Artery Stenosis Detection Method with a Hybrid LSTM-CNN Model

Abstract

Screening for coronary artery disease is a major health issue, knowing that the most common cause of death in industrialized countries is cardiovascular pathology (coronary artery disease, stroke, other cardiovascular diseases). Computer Aided Diagnosis systems (CADx) can assist cardiologists to and play a key role in detecting abnormalities and treating coronary arteries. In this paper we propose a deep learning classification method based on a new Hybrid CNN-LSTM Architecture. The aim of our method is to detect the presence of stenosis in the coronary arteries and to classify the type of arteries. Our experiments have been conducted using an anonymized database from a Belgian hospital (CHR Mons-Hainaut) thanks to a retrospective study.

Citation

Xavier Lessage, Michal Nedoszytko, Saïd Mahmoudi, Lilian Marey, Olivier Debauche, Sidi Ahmed Mahmoudi. A New Coronary Artery Stenosis Detection Method with a Hybrid LSTM-CNN Model. Artificial Intelligence and Smart Environment (ICAISE 2022), Lecture Notes in Networks and Systems, Springer, pp. 70–77.