Hybrid classical–quantum Convolutional Neural Network for stenosis detection in X-ray coronary angiography

作者：

Highlights：

• A quantum network boosts the performance of classical neural architecture.

• An L2 hyperbolic tangent layer bounds the features between classical and quantum stages.

• The X-ray angiography images are analyzed to develop a robust stenosis detector system.

• Transfer learning and quantum network substantially improved stenosis detection.

• An efficient hybrid classical–quantum architecture is focused on stenosis detection.

摘要

•A quantum network boosts the performance of classical neural architecture.•An L2 hyperbolic tangent layer bounds the features between classical and quantum stages.•The X-ray angiography images are analyzed to develop a robust stenosis detector system.•Transfer learning and quantum network substantially improved stenosis detection.•An efficient hybrid classical–quantum architecture is focused on stenosis detection.

论文关键词：Quantum computing,Hybrid Convolutional Neural Network,Coronary angiography,Stenosis detection,X-ray imaging

论文评审过程：Received 4 July 2021, Revised 3 September 2021, Accepted 16 October 2021, Available online 28 October 2021, Version of Record 2 November 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116112