融合残差与空间卷积的新型Transformer模型，提升猴痘皮肤图像诊断准确率。
RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis
- 结合定制Swin Transformer、残差块和空间块，捕捉全局与局部特征。
- 在多类皮肤疾病数据集上达97.8%准确率，显著优于现有CNN与ViT模型。
- 适合医疗影像分析场景，助力快速精准识别猴痘病例。
猴痘已成为全球关注的公共卫生问题，病例每日持续增加。传统检测方法如PCR和人工检查存在敏感性低、成本高、工作量大等问题。深度学习虽提供自动化解决方案，但数据稀缺、纹理与对比度差异大，且易与其他皮肤病混淆。为此，提出一种新混合方法（RS-FME-SwinT），融合残差学习与空间卷积神经网络（CNN），结合定制Swin Transformer以捕获多尺度全局与局部相关特征。该方法采用基于迁移学习的特征图增强（FME）技术，利用定制化SwinT提取全局信息，残差块提取纹理，空间块捕捉局部对比度变化，并引入逆残差块有效缓解梯度消失，增强局部模式建模能力。所提模型系统性降低同类猴痘图像内部差异，实现与其他皮肤疾病的精准区分。在多样化猴痘数据集上进行留出交叉验证，结果表明其准确率达97.80%，敏感性96.82%，精确率98.06%，F-score为97.44%，显著超越当前主流CNN与视觉变压器（ViT）模型。该模型可为医疗从业者提供快速准确的猴痘诊断工具，有力支持防控工作。
原文摘要 · Abstract (English)
Monkeypox (MPox) has emerged as a significant global concern, with cases steadily increasing daily. Conventional detection methods, including polymerase chain reaction (PCR) and manual examination, exhibit challenges of low sensitivity, high cost, and substantial workload. Therefore, deep learning offers an automated solution; however, the datasets include data scarcity, texture, contrast, inter-intra class variability, and similarities with other skin infectious diseases. In this regard, a novel hybrid approach is proposed that integrates the learning capacity of Residual Learning and Spatial Exploitation Convolutional Neural Network (CNN) with a customized Swin Transformer (RS-FME-SwinT) to capture multi-scale global and local correlated features for MPox diagnosis. The proposed RS-FME-SwinT technique employs a transfer learning-based feature map enhancement (FME) technique, integrating the customized SwinT for global information capture, residual blocks for texture extraction, and spatial blocks for local contrast variations. Moreover, incorporating new inverse residual blocks within the proposed SwinT effectively captures local patterns and mitigates vanishing gradients. The proposed RS-FME-SwinT has strong learning potential of diverse features that systematically reduce intra-class MPox variation and enable precise discrimination from other skin diseases. Finally, the proposed RS-FME-SwinT is a holdout cross-validated on a diverse MPox dataset and achieved outperformance on state-of-the-art CNNs and ViTs. The proposed RS-FME-SwinT demonstrates commendable results of an accuracy of 97.80%, sensitivity of 96.82%, precision of 98.06%, and an F-score of 97.44% in MPox detection. The RS-FME-SwinT could be a valuable tool for healthcare practitioners, enabling prompt and accurate MPox diagnosis and contributing significantly to mitigation efforts.
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