用预训练CNN和XAI技术，从皮疹图像中早期识别猴痘，准确率超93%。
An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
- 用迁移学习微调VGG、Inception等CNN模型，适配猴痘检测任务
- InceptionV3在二分类中达95%准确率，MobileNetV2在多分类中达93%
- 结合Grad-CAM可视化关键病灶区域，提升模型可解释性
猴痘是由猴痘病毒引起的一种人畜共患疾病，临床表现与多种皮肤病症相似，早期精准诊断困难。人工智能尤其是深度学习在医学图像分析中具有潜力，但针对猴痘的预训练卷积神经网络（CNN）及可解释性人工智能（XAI）技术应用仍不充分。本研究评估了VGG16、VGG19、InceptionV3、MobileNetV2等预训练CNN模型在二分类与多分类数据集上的猴痘早期检测效果，并利用Grad-CAM技术提升模型可解释性。采用MSLD和MSLD v2.0数据集进行训练与验证，通过冻结初始层并添加自定义层实现迁移学习，避免过拟合。模型性能以准确率、精确率、召回率、F1分数和ROC曲线评估。结果显示，InceptionV3在二分类数据集上准确率达95%，MobileNetV2在多分类数据集上准确率达93%。Grad-CAM成功定位关键病变区域。尽管准确率高，部分模型仍表现出过拟合迹象，表现为训练与验证损失差异明显。结论表明，预训练CNN在猴痘检测中具有潜力，结合XAI可增强诊断可靠性与模型透明度。未来工作应改进数据集局限性，融合多模态数据，并探索更多可解释性方法。
原文摘要 · Abstract (English)
Context: Mpox is a zoonotic disease caused by the Mpox virus, which shares similarities with other skin conditions, making accurate early diagnosis challenging. Artificial intelligence (AI), especially Deep Learning (DL), has a strong tool for medical image analysis; however, pre-trained models like CNNs and XAI techniques for mpox detection is underexplored. Objective: This study aims to evaluate the effectiveness of pre-trained CNN models (VGG16, VGG19, InceptionV3, MobileNetV2) for the early detection of monkeypox using binary and multi-class datasets. It also seeks to enhance model interpretability using Grad-CAM an XAI technique. Method: Two datasets, MSLD and MSLD v2.0, were used for training and validation. Transfer learning techniques were applied to fine-tune pre-trained CNN models by freezing initial layers and adding custom layers for adapting the final features for mpox detection task and avoid overfitting. Models performance were evaluated using metrics such as accuracy, precision, recall, F1-score and ROC. Grad-CAM was utilized for visualizing critical features. Results: InceptionV3 demonstrated the best performance on the binary dataset with an accuracy of 95%, while MobileNetV2 outperformed on the multi-class dataset with an accuracy of 93%. Grad-CAM successfully highlighted key image regions. Despite high accuracy, some models showed overfitting tendencies, as videnced by discrepancies between training and validation losses. Conclusion: This study underscores the potential of pre-trained CNN models in monkeypox detection and the value of XAI techniques. Future work should address dataset limitations, incorporate multimodal data, and explore additional interpretability techniques to improve diagnostic reliability and model transparency
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。