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

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..

Medienart:

Preprint

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

arXiv.org - (2023) vom: 29. Juli Zur Gesamtaufnahme - year:2023

Sprache:

Englisch

Beteiligte Personen:

Cheung, Wing Keung [VerfasserIn]
Kalindjian, Jeremy [VerfasserIn]
Bell, Robert [VerfasserIn]
Nair, Arjun [VerfasserIn]
Menezes, Leon J. [VerfasserIn]
Patel, Riyaz [VerfasserIn]
Wan, Simon [VerfasserIn]
Chou, Kacy [VerfasserIn]
Chen, Jiahang [VerfasserIn]
Torii, Ryo [VerfasserIn]
Davies, Rhodri H. [VerfasserIn]
Moon, James C. [VerfasserIn]
Alexander, Daniel C. [VerfasserIn]
Jacob, Joseph [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

000
620
Computer Science - Machine Learning
Electrical Engineering and Systems Science - Image and Video Processing

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XAR040385647