用集成学习提升皮肤病变分类准确率，最高达86.7%。
Enhancing Skin Lesion Diagnosis with Ensemble Learning
- 组合多个预训练模型，通过堆叠提升分类性能
- 新模型SkinNet在HAM10000数据集上达86.7%准确率
- 适合医学影像分析与深度学习应用研究者参考
皮肤病变是日益重要的医疗问题，其严重程度从良性到恶性差异显著。准确诊断对及时治疗至关重要。本研究利用包含七类病变的HAM10000数据集，评估了三种预训练模型：MobileNetV2、ResNet18和VGG11，准确率分别为0.798、0.802和0.805。为进一步提升分类准确率，构建了采用最大投票、平均投票和堆叠的集成模型，准确率分别达到0.803、0.82和0.83。基于表现最佳的堆叠模型，提出SkinNet模型，结合定制架构与微调，在测试集上实现0.867的准确率和0.96的AUC。该结果显著优于单一模型，验证了集成学习在皮肤病变分类中的有效性。
原文摘要 · Abstract (English)
Skin lesions are an increasingly significant medical concern, varying widely in severity from benign to cancerous. Accurate diagnosis is essential for ensuring timely and appropriate treatment. This study examines the implementation of deep learning methods to assist in the diagnosis of skin lesions using the HAM10000 dataset, which contains seven distinct types of lesions. First, we evaluated three pre-trained models: MobileNetV2, ResNet18, and VGG11, achieving accuracies of 0.798, 0.802, and 0.805, respectively. To further enhance classification accuracy, we developed ensemble models employing max voting, average voting, and stacking, resulting in accuracies of 0.803, 0.82, and 0.83. Building on the best-performing ensemble learning model, stacking, we developed our proposed model, SkinNet, which incorporates a customized architecture and fine-tuning, achieving an accuracy of 0.867 and an AUC of 0.96. This substantial improvement over individual models demonstrates the effectiveness of ensemble learning in improving skin lesion classification.
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