🤖 AI Summary
This study addresses the high computational complexity of deep learning models in breast cancer detection, which incurs substantial energy consumption and carbon emissions, necessitating a balance between performance and environmental sustainability. For the first time in this domain, we jointly evaluate the accuracy and CO₂ emissions of seven mainstream architectures—including CNNs, Transformers, and hybrid models—on the Breast Ultrasound and BreakHis 400X datasets. Our results demonstrate that DeiT achieves the best trade-off between efficiency and accuracy on ultrasound data, while ViT and Swin perform optimally on BreakHis. The absence of a universally superior architecture across both modalities underscores the importance of multidimensional model selection that accounts for both data characteristics and environmental impact.
📝 Abstract
Recent advances in machine learning have greatly improved breast cancer detection, enabling more accurate and timely diagnosis. Deep learning (DL) models show strong potential for medical image analysis; however, as their architectural complexity increases, their environmental impacts are becoming a growing concern. In this paper, we present a comparative analysis of seven DL models for breast cancer detection on two medical datasets: Breast Ultrasound and BreakHis 400X. The evaluated architectures range from Convolutional Neural Networks (CNNs) and transformers to hybrid models. In addition to performance metrics, we assess CO2 emissions during both training and inference. Our results show that EfficientNet and ResNet consistently deliver strong performance, although with higher CO2 emissions. The selected transformers, such as DeiT-Tiny, perform competitively on both datasets, whereas DenseNet121 achieves lower accuracy. On the Breast Ultrasound Dataset, DeiT provides the most favourable balance between accuracy and energy consumption, whereas on the BreakHis dataset, the ViT and Swin models achieve the best results. Overall, our findings indicate that no single architecture category from the evaluated ones consistently dominates across the two selected datasets. Our results highlight the importance of jointly considering performance, emissions, and dataset characteristics when selecting models for medical applications.