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Deep learning models for breast cancer detection benchmarked for performance and emissions

By PulseAugur Editorial · [1 sources] ·

A new paper benchmarks seven deep learning models for breast cancer detection, evaluating their performance and environmental impact. The study found that while EfficientNet and ResNet offer strong accuracy, they also produce higher CO2 emissions. Transformer models like DeiT-Tiny, ViT, and Swin Transformer showed competitive results, with DeiT-Tiny offering a good balance of accuracy and energy efficiency on one dataset, and ViT and Swin excelling on another. The research emphasizes the need to consider performance, emissions, and dataset specifics when choosing models for medical applications. AI

IMPACT Highlights the trade-offs between model performance, energy consumption, and dataset characteristics in medical AI applications.

RANK_REASON Academic paper evaluating deep learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning models for breast cancer detection benchmarked for performance and emissions

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Academic paper evaluating deep learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Samar Garrab, Ghada Achour ·

    Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection

    arXiv:2608.09996v1 Announce Type: cross 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 architec…

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