As dermatological conditions become increasingly common and the availability of dermatologists remains limited, there is a growing need for intelligent tools to support both patients and clinicians in the timely and accurate diagnosis of skin diseases. In this project, we developed a deep learning based model for the classification and diagnosis of skin conditions. By leveraging pretraining on publicly available skin disease image datasets, our model effectively extracted visual features and accurately classified various dermatological cases. Throughout the project, we refined the model architecture, optimized data preprocessing workflows, and applied targeted data augmentation techniques to improve overall performance. The final model, based on the Swin Transformer, achieved a prediction accuracy of 87.71 percent across eight skin lesion classes on the ISIC2019 dataset. These results demonstrate the model's potential as a diagnostic support tool for clinicians and a self assessment aid for patients.

翻译：随着皮肤疾病的日益普遍和皮肤科医生资源的持续紧缺，开发智能工具以辅助患者和临床医生实现皮肤疾病的及时准确诊断变得愈发重要。在本项目中，我们开发了一种基于深度学习的皮肤疾病分类与诊断模型。通过利用公开皮肤疾病图像数据集进行预训练，我们的模型能够有效提取视觉特征并准确分类多种皮肤科病例。在整个项目过程中，我们优化了模型架构，改进了数据预处理流程，并应用了针对性的数据增强技术以提升整体性能。基于Swin Transformer的最终模型在ISIC2019数据集的八类皮肤病变分类任务中达到了87.71%的预测准确率。这些结果表明，该模型具备作为临床医生诊断辅助工具和患者自我评估手段的潜力。