ResNet50 fine-tuned for enhanced melanoma detection

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]

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• arXiv
• melanoma
• ResNet50
• streamlit

• paper
• model release
• product

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