aceEffect of Deep Learning Training Policy on Greenhouse Gas Emissions and Carbon Efficiency for Chest Radiograph Classification
Purpose Environmental sustainability is an emerging priority in radiology, yet the impact of deep learning training policies on greenhouse gas emissions remains poorly characterized. This study quantified the effect of training policy on carbon dioxide equivalent (CO 2 eq) emissions and model performance for chest radiograph classification. Methods Anteroposterior chest radiographs (128 907 training, 24 570 validation, 8282 test) were used to train 3 ImageNet-pretrained convolutional neural networks (ResNet-50,…
Prospective early stopping with patience 10 preserved chest radiograph classification performance while lowering total training emissions by up to 38% and raising carbon efficiency by up to 76% compared to fixed 20-epoch training.
Fixed 20-epoch training without checkpoint selection wasted most compute, with 78% to 84% of total emissions occurring after the optimal checkpoint had already been reached.
Evidence
- Peer-reviewedCanadian Association of Radiologists Journal2026-08-12
How should this claim be treated?
Truvace Impact Record TRV-2026-0753, v1: “Effect of Deep Learning Training Policy on Greenhouse Gas Emissions and Carbon Efficiency for Chest Radiograph Classification.” Truvace, 2026-08-14. /record/TRV-2026-0753 (accessed at citation time). sha256 b1a75daf9e65ad7a…
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