用深度学习和视觉Transformer识别猴痘皮损，提升早期诊断准确率。
Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning and Vision Transformers: A Comprehensive Analysis
- 从零训练模型受限于数据，转而使用迁移学习提升性能。
- MobileNet-v2达93.15%准确率，优于其他主流模型。
- 结合可解释AI技术，增强模型决策可信度，适合医疗辅助场景。
猴痘是一种可通过人际传播的动物源性病毒性疾病，因其症状与麻疹、水痘高度相似，早期临床诊断困难。医学影像结合深度学习技术在分析皮损区域方面展现出提升疾病检测的潜力。本研究探索了利用公开皮肤病变图像数据集，从零开始训练深度学习与视觉变换器（Vision Transformer）模型的可行性。实验结果表明，数据集规模限制是构建高性能分类模型的主要瓶颈。通过引入预训练模型的迁移学习策略，显著提升了分类性能：MobileNet-v2达到93.15%准确率与93.09%加权平均F1分数；ViT B16与ResNet-50分别取得92.12%和86.21%的准确率，表现优于已有研究。为进一步验证模型可靠性，应用可解释人工智能技术对预测结果进行分析。
原文摘要 · Abstract (English)
Since mpox can spread from person to person, it is a zoonotic viral illness that poses a significant public health concern. It is difficult to make an early clinical diagnosis because of how closely its symptoms match those of measles and chickenpox. Medical imaging combined with deep learning (DL) techniques has shown promise in improving disease detection by analyzing affected skin areas. Our study explore the feasibility to train deep learning and vision transformer-based models from scratch with publicly available skin lesion image dataset. Our experimental results show dataset limitation as a major drawback to build better classifier models trained from scratch. We used transfer learning with the help of pre-trained models to get a better classifier. The MobileNet-v2 outperformed other state of the art pre-trained models with 93.15% accuracy and 93.09% weighted average F1 score. ViT B16 and ResNet-50 also achieved satisfactory performance compared to already available studies with accuracy 92.12% and 86.21% respectively. To further validate the performance of the models, we applied explainable AI techniques.
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