A 3D deep learning classifier and its explainability when assessing coronary artery disease

• URL: http://arxiv.org/abs/2308.00009v1
• Date: Sat, 29 Jul 2023 14:54:50 GMT
• Title: A 3D deep learning classifier and its explainability when assessing coronary artery disease
• Authors: Wing Keung Cheung, Jeremy Kalindjian, Robert Bell, Arjun Nair, Leon J. Menezes, Riyaz Patel, Simon Wan, Kacy Chou, Jiahang Chen, Ryo Torii, Rhodri H. Davies, James C. Moon, Daniel C. Alexander, Joseph Jacob
• Abstract summary: Our proposed method outperforms a 2D Resnet-50 model by 23.65%. We link the 3D CAD classification to a 2D two-class semantic segmentation for improved explainability and accurate abnormality localisation.
• Score: 2.854890811393726
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs. In this study, we propose a 3D Resnet-50 deep learning model to directly classify normal subjects and CAD patients on computed tomography coronary angiography images. Our proposed method outperforms a 2D Resnet-50 model by 23.65%. Explainability is also provided by using a Grad-GAM. Furthermore, we link the 3D CAD classification to a 2D two-class semantic segmentation for improved explainability and accurate abnormality localisation.

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