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ResNet-50 enhanced with ASFF improves skin lesion classification accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an enhanced ResNet-50 model incorporating Adaptive Spatial Feature Fusion (ASFF) to improve the accuracy of skin lesion classification. This model adaptively integrates multi-scale features, focusing on lesion-relevant regions to reduce overfitting and enhance representation. Tested on the ISIC 2020 dataset, the ASFF-based ResNet-50 achieved 93.182% accuracy, outperforming baseline models and demonstrating strong generalization on the ISIC 2019 dataset. AI

IMPACT Enhances computer-aided diagnosis systems, potentially leading to earlier and more accurate skin cancer detection.

RANK_REASON The cluster contains an academic paper detailing a new model architecture and its evaluation on benchmark datasets. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

ResNet-50 enhanced with ASFF improves skin lesion classification accuracy

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The cluster contains an academic paper detailing a new model architecture and its evaluation on benchmark datasets. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Runhao Liu, Fengyi Zha, Fei Ding, Guangzhen Yao, Peng Zhang ·

    Skin Lesion Classification Based on ResNet-50 Enhanced With Adaptive Spatial Feature Fusion

    arXiv:2510.03876v2 Announce Type: replace Abstract: Skin cancer classification is challenging due to high inter-class similarity, intra-class variability, and artifacts in dermoscopic images. To address these issues, we propose an improved ResNet-50 with Adaptive Spatial Feature …

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