用深度学习辅助皮肤疾病诊断，提升准确率至87.71%
Towards Automated Differential Diagnosis of Skin Diseases Using Deep Learning and Imbalance-Aware Strategies
- 基于Swin Transformer的模型，结合预训练与数据增强
- 在ISIC2019数据集上达87.71%分类准确率
- 适合临床辅助诊断与患者自检使用
随着皮肤病日益普遍而皮肤科医生资源有限，亟需智能工具辅助及时准确诊断。本研究开发了一种基于深度学习的皮肤病变分类模型。通过在公开皮肤图像数据集上进行预训练，模型有效提取视觉特征并准确识别多种皮肤病。项目中优化了模型结构、数据预处理流程，并采用针对性数据增强策略以提升性能。最终基于Swin Transformer的模型在ISIC2019数据集上对八类皮肤病灶实现87.71%的预测准确率。结果表明该模型具备作为临床辅助诊断工具及患者自我评估工具的潜力。
原文摘要 · Abstract (English)
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.
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