In this paper, a deep learning approach for Mpox diagnosis named Customized Residual SwinTransformerV2 (RSwinV2) has been proposed, trying to enhance the capability of lesion classification by employing the RSwinV2 tool-assisted vision approach. In the RSwinV2 method, a hierarchical structure of the transformer has been customized based on the input dimensionality, embedding structure, and output targeted by the method. In this RSwinV2 approach, the input image has been split into non-overlapping patches and processed using shifted windows and attention in these patches. This process has helped the method link all the windows efficiently by avoiding the locality issues of non-overlapping regions in attention, while being computationally efficient. RSwinV2 has further developed based on SwinTransformer and has included patch and position embeddings to take advantage of the transformer global-linking capability by employing multi-head attention in these embeddings. Furthermore, RSwinV2 has developed and incorporated the Inverse Residual Block (IRB) into this method, which utilizes convolutional skip connections with these inclusive designs to address the vanishing gradient issues during processing. RSwinV2 inclusion of IRB has therefore facilitated this method to link global patterns as well as local patterns; hence, its integrity has helped improve lesion classification capability by minimizing variability of Mpox and increasing differences of Mpox, chickenpox, measles, and cowpox. In testing SwinV2, its accuracy of 96.21 and an F1score of 95.62 have been achieved on the Kaggle public dataset, which has outperformed standard CNN models and SwinTransformers; RSwinV2 vector has thus proved its valiance as a computer-assisted tool for Mpox lesion observation interpretation.

翻译：本文提出了一种用于猴痘诊断的深度学习方法，称为定制化残差SwinTransformerV2（RSwinV2），旨在通过采用RSwinV2工具辅助视觉方法增强病变分类能力。在RSwinV2方法中，根据输入维度、嵌入结构以及方法的目标输出，对Transformer的层次结构进行了定制。在此RSwinV2方法中，输入图像被分割成不重叠的图块，并使用这些图块中的移位窗口和注意力机制进行处理。这一过程通过避免注意力机制中非重叠区域的局部性问题，帮助该方法高效地连接所有窗口，同时保持计算效率。RSwinV2在SwinTransformer的基础上进一步发展，包含了图块嵌入和位置嵌入，通过在这些嵌入中采用多头注意力机制，以利用Transformer的全局连接能力。此外，RSwinV2开发并融入了逆残差块（IRB）到该方法中，该模块利用卷积跳跃连接与这些包容性设计，以解决处理过程中的梯度消失问题。因此，RSwinV2对IRB的纳入促进了该方法连接全局模式以及局部模式；其完整性通过最小化猴痘的变异性和增加猴痘、水痘、麻疹及牛痘之间的差异性，从而帮助提升了病变分类能力。在测试中，SwinV2在Kaggle公共数据集上取得了96.21的准确率和95.62的F1分数，其性能超越了标准CNN模型和SwinTransformers；RSwinV2向量由此证明了其作为猴痘病变观察解读的计算机辅助工具的有效性。