In this study, we present an innovative approach for enhancing skin cancer classification through a multi‐branch architecture inspired by ShuffleNet. Our methodology focuses on improving feature extraction and representation, emphasizing cross‐channel information exchange to achieve superior accuracy. The architecture comprises three branches: a primary feature enhancement branch, a parallel feature enhancement branch, and a global feature aggregation branch. We employ transposed convolution layers for upsampling, cross‐channel normalization, and the Swish activation function to enhance feature representations. Channel shuffle operations and group convolutions stimulate cross‐channel information exchange in both branches. The global feature aggregation branch utilizes global average pooling and depth concatenation to combine features from all branches. Subsequent Swish activation, followed by fully connected layers and softmax activation, yields class probabilities for precise skin cancer classification. This multi‐branch framework offers a promising avenue for accurate and informed medical image analysis in skin cancer detection. Integral to this research is the utilization of the ISIC2019 and ISIC 2020 datasets, encompassing diverse dermoscopic images from multiple sources. By leveraging these datasets, the methodology capitalizes on comprehensive data for precise skin cancer detection, thereby advancing medical image analysis.

中文翻译：

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利用多分支 ShuffleNet 架构推进皮肤癌诊断

在这项研究中，我们提出了一种创新方法，通过受 ShuffleNet 启发的多分支架构来增强皮肤癌分类。我们的方法侧重于改进特征提取和表示，强调跨渠道信息交换以实现卓越的准确性。该架构包括三个分支：主要特征增强分支、并行特征增强分支和全局特征聚合分支。我们采用转置卷积层进行上采样、跨通道归一化和 Swish 激活函数来增强特征表示。通道洗牌操作和组卷积刺激两个分支中的跨通道信息交换。全局特征聚合分支利用全局平均池化和深度串联来组合来自所有分支的特征。随后的 Swish 激活、全连接层和 softmax 激活，产生精确皮肤癌分类的类概率。这种多分支框架为皮肤癌检测中准确且知情的医学图像分析提供了一条有前途的途径。这项研究的一个组成部分是利用 ISIC2019 和 ISIC 2020 数据集，其中包含来自多个来源的各种皮肤镜图像。通过利用这些数据集，该方法利用全面的数据进行精确的皮肤癌检测，从而推进医学图像分析。