10 min | Latest | RadOnc | Publication Source

• Podcast based on: Chiu T-M, Li Y-C, Chi I-C, Tseng M-H. AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data. Cancers. 2025; 17(1):137. https://doi.org/10.3390/cancers17010137 Type: Article | Publication date: Jan 3, 2025
• Summary: This research paper details the development and validation of an AI model for skin cancer diagnosis using dermoscopic images. A two-stage classification approach, employing an ensemble of pre-trained convolutional neural networks and vision transformers, significantly improved diagnostic accuracy and drastically reduced false negatives in both a Western (ISIC) and Eastern (CSMUH) dataset. The model distinguishes between melanoma, non-melanoma skin cancers, and benign cases, aiding clinicians in prioritising treatment. The study highlights the potential for AI to enhance skin cancer diagnosis, particularly in resource-constrained settings, though limitations regarding computational demands and dataset size are acknowledged.
• Keywords: malignant melanoma; dermoscopic images; voting ensemble learning; two-stage classification strategy

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