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ResNet50 fine-tuned for enhanced melanoma detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel two-stage fine-tuning method for the ResNet50 model to improve the detection of melanoma from dermoscopic images. This approach addresses challenges like class imbalance and suboptimal transfer learning by first training only the classification head and then fine-tuning all layers at a low learning rate. The model achieved a high AUC-ROC of 0.9559 and demonstrated significant improvements in sensitivity compared to single-stage fine-tuning, with a fully deployable Streamlit application provided. AI

IMPACT Enhances AI's role in early disease detection, potentially improving diagnostic accuracy and patient outcomes.

RANK_REASON The cluster contains an academic paper detailing a new method for an existing model. [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 →

ResNet50 fine-tuned for enhanced melanoma detection

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The cluster contains an academic paper detailing a new method for an existing model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Aryan Bhagat ·

    Two-Stage Fine-Tuning of ResNet50 for High-Sensitivity Melanoma Detection on Dermoscopic Images

    arXiv:2606.17504v1 Announce Type: cross Abstract: Melanoma is the most dangerous form of skin cancer with five-year survival rates exceeding 99% when detected early but falling sharply once the disease spreads. This paper proposes and evaluates a two-stage fine-tuning approach fo…

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