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Deep learning models benchmarked for breast cancer diagnosis

By PulseAugur Editorial · [1 sources] ·

Researchers have evaluated ten deep learning models, including CNNs like DenseNet121 and transformer models such as ViT and Swin Transformer, for their effectiveness in diagnosing breast cancer from medical images. DenseNet121 achieved the highest accuracy at 99.4% with an AUC-ROC of 0.999, significantly outperforming other models, particularly the transformer-based architectures. The study also employed explainable AI techniques like Grad-CAM and LIME to ensure the models' predictions were interpretable and focused on diagnostically relevant regions, suggesting a path toward accurate and understandable diagnostic tools. AI

IMPACT Enhances trust and potential clinical adoption of AI in medical diagnostics through improved interpretability.

RANK_REASON Academic paper detailing model performance and explainability for a specific task. [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 benchmarked for breast cancer diagnosis

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Academic paper detailing model performance and explainability for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sami Alnawali, Muhammad Atif Saeed, Abdul Hadi, Azka Atiq, Akhtar Jamil, Ahmad Din ·

    Interpretable Deep Learning Models for Breast Cancer Diagnosis Using Medical Imaging

    arXiv:2610.37026v1 Announce Type: cross Abstract: Deep learning shows promise for breast cancer diagnosis, but limited interpretability hinders clinical adoption. We benchmark ten ImageNet-pretrained models (ResNet50, VGG19, InceptionV3, DenseNet121, Xception, MobileNetV2, Effici…

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