Abstract
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
Abstract (translated)
**翻译** 背景：猴痘是一种由猴痘病毒引起的动物源性疾病，其临床表现与其他皮肤疾病相似，使得早期准确诊断变得具有挑战性。人工智能（AI），特别是深度学习（DL）在医学图像分析方面表现出强大的能力；然而，预训练模型如卷积神经网络（CNNs）和用于猴痘检测的可解释的人工智能（XAI）技术目前研究较少。 **目的：** 本研究旨在评估VGG16、VGG19、InceptionV3和MobileNetV2等预训练CNN模型在二分类和多分类数据集上早期诊断猴痘的有效性。同时，利用梯度加权类激活映射（Grad-CAM）这一XAI技术提高模型的可解释性。 **方法：** 研究使用了MSLD和MSLD v2.0两个数据集进行训练和验证。通过应用迁移学习技术，冻结预训练CNN模型的初始层，并添加自定义层以适应最终特征用于猴痘检测任务，从而避免过拟合现象。评估模型性能时采用了准确性、精确度、召回率、F1分数及ROC等指标；同时利用Grad-CAM进行关键图像区域可视化。 **结果：** 在二分类数据集上，InceptionV3表现最佳，准确率达到95%；而在多分类数据集上，MobileNetV2表现出色，其准确率为93%。通过Grad-CAM成功地识别出了重要的图像特征。尽管大多数模型展现了较高的准确性，但某些模型仍然显示出过拟合的倾向，这从训练损失与验证损失之间的差异可以明显看出。 **结论：** 本研究强调了预训练CNN模型在猴痘诊断中的潜力以及XAI技术的价值。未来的工作应当解决数据集的局限性，引入多模态数据，并探索额外的可解释性方法以提高诊断可靠性和模型透明度。
URL
https://arxiv.org/abs/2507.15915
