Skin Cancer Classification Using Explainable Artificial Intelligence With an Ensemble Model and Rigorous Leakage Free Validation
· 2026-09-05 · 原文
DOI：10.64898/2026.09.02.26362011v1?rss=1
Background: Reliable melanoma classification requires models that capture both local dermoscopic morphology and broader contextual patterns while maintaining auditable, leakageaware internal validation. Objectives: To develop and internally validate an EfficientNetB0-Swin Transformer Tiny ensemble for classifying histopathologically verified dermoscopic images as benign melanocytic lesions or malignant melanoma. Methods: This retrospective diagnostic model-development and internal validation study screened 552,869 ISIC Archive records; filtering and dermatologist review yielded 1,199 uniquepatient and unique lesion images (578 benign and 621 malignant). Images were the predictors and histopathology was the reference. ImageNet pretrained EfficientNetB0 and Swin-T features were fused. Patien
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1. 人话版
Background: Reliable melanoma classification requires models that capture both local dermoscopic morphology and broader contextual patterns while maintaining auditable, leakageaware internal validation.
Objectives: To develop and internally validate an EfficientNetB0-Swin Transformer Tiny ensemble for classifying histopathologically verified dermoscopic images as benign melanocytic lesions or malignant melanoma.
2. 领域脉络
来源板块：板块一 · 研究前沿。
3. 机制拆解
Images were the predictors and histopathology was the reference.
4. 证据与数字
Methods: This retrospective diagnostic model-development and internal validation study screened 552,869 ISIC Archive records; filtering and dermatologist review yielded 1,199 uniquepatient and unique lesion images (578 benign and 621 malignant).
ImageNet pretrained EfficientNetB0 and Swin-T features were fused.
5. 反例与边界
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6. 跨领域连接与意外收获
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