Abstract：
Skin cancer is one of the common types of cancer. It spreads quickly and is not easy to detect in the early stages, posing a major threat to human health. In recent years, deep learning methods have attracted widespread attention for skin cancer detection in dermoscopic images. However, training a practical classifier becomes highly challenging due to inter-class similarity and intra-class variation in skin lesion images. To address these problems, we propose a multi-scale fusion structure that combines shallow and deep features for more accurate classification. Simultaneously, we implement three approaches to the problem of class imbalance: class weighting, label smoothing, and resampling. In addition, the HAM10000_RE dataset strips out hair features to demonstrate the role of hair features in the classification process. We demonstrate that the region of interest is the most critical classification feature for the HAM10000_SE dataset, which segments lesion regions. We evaluated the effectiveness of our model using the HAM10000 and ISIC2019 dataset. The results showed that this method performed well in dermoscopic classification tasks, with ACC and AUC of 94.0% and 99.3%, on the HAM10000 dataset and ACC of 89.8% for the ISIC2019 dataset. The overall performance of our model is excellent in comparison to state-of-the-art models.
摘要：
皮肤癌是常见的癌症类型之一。它传播迅速，在早期阶段不容易发现，对人类健康构成重大威胁。近年来,深度学习方法在皮肤镜图像中的皮肤癌检测中引起了广泛的关注。然而,由于皮肤病变图像中的类间相似性和类内变化，训练实用的分类器变得非常具有挑战性。为了解决这些问题,我们提出了一种结合浅层和深层特征的多尺度融合结构，以实现更准确的分类。同时,我们实现了三种方法来解决类不平衡的问题：类加权，标签平滑，和重新采样。此外,HAM10000_RE数据集剥离了头发特征，以证明头发特征在分类过程中的作用。我们证明了感兴趣的区域是HAM10000_SE数据集的最关键的分类特征，划分病变区域。我们使用HAM10000和ISIC2019数据集评估了我们模型的有效性。结果表明，该方法在皮肤分类任务中表现良好，ACC和AUC分别为94.0%和99.3%，在ISIC2019数据集中的HAM10000数据集和ACC为89.8%。与最先进的模型相比，我们模型的整体性能非常出色。
