Deep Learning Models Compared for Skin Cancer Detection

Researchers have conducted a comprehensive evaluation of twelve deep learning models for skin cancer detection, comparing convolutional neural networks (CNNs), vision transformers (ViTs), hybrid models, and vision-language models (VLMs). The study utilized the PAD-UFES-20 dataset and assessed performance using metrics like AUC, F1 score, and sensitivity at 80% specificity. Results indicate that while CNNs offer a solid baseline, transformer-based architectures generally provide superior discrimination. Hybrid models such as MaxViT Tiny and CoAtNet0, along with a SigLIP-based VLM, demonstrated the best overall performance for clinical deployment, with CLIP-based models showing high precision. AI

IMPACT Provides practical guidance on selecting AI models for real-world skin cancer screening, potentially improving early detection rates.

RANK_REASON The cluster is based on an academic paper presenting a comparative study of AI models on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• CNNs
• CoAtNet0
• MaxViT Tiny
• PAD-UFES-20 dataset
• SigLIP
• Skin Cancer
• Transformers
• Vision Language Models

• paper
• model release

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