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Abstract：
Skin lesion classification is vital for the early detection and diagnosis of skin diseases, facilitating timely intervention and treatment. However, existing classification methods face challenges in managing complex information and long-range dependencies in dermoscopic images. Therefore, this research aims to enhance the feature representation by incorporating local, global, and hierarchical features to improve the performance of skin lesion classification. We introduce a novel dual-track deep learning (DL) model in this research for skin lesion classification. The first track utilizes a modified Densenet-169 architecture that incorporates a Coordinate Attention Module (CoAM). The second track employs a customized convolutional neural network (CNN) comprising a Feature Pyramid Network (FPN) and Global Context Network (GCN) to capture multiscale features and global contextual information. The local features from the first track and the global features from second track are used for precise localization and modeling of the long-range dependencies. By leveraging these architectural advancements within the DenseNet framework, the proposed neural network achieved better performance compared to previous approaches. The network was trained and validated using the HAM10000 dataset, achieving a classification accuracy of 93.2%.
摘要：
皮肤病变分类对于皮肤疾病的早期发现和诊断至关重要。及时干预和治疗。然而,现有的分类方法在管理复杂的信息和皮肤图像中的远程依赖关系方面面临挑战。因此,这项研究旨在通过结合本地，全球,和分层特征，以提高皮肤病变分类的性能。我们在本研究中引入了一种新颖的双轨深度学习（DL）模型，用于皮肤病变分类。第一首曲目采用了经过修改的Densenet-169架构，该架构包含了协调注意模块（CoAM）。第二轨道采用包括特征金字塔网络(FPN)和全局上下文网络(GCN)的定制卷积神经网络(CNN)来捕获多尺度特征和全局上下文信息。来自第一轨道的局部特征和来自第二轨道的全局特征用于远程依赖性的精确定位和建模。通过利用DenseNet框架中的这些架构进步，与以前的方法相比，所提出的神经网络实现了更好的性能。使用HAM10000数据集训练和验证网络，达到93.2%的分类准确率。
