Towards Automated Differential Diagnosis of Skin Diseases Using Deep Learning and Imbalance-Aware Strategies

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 was able to effectively extract visual features and accurately classify 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% across eight skin lesion classes on the ISIC2019 dataset. These results demonstrate the model’s potential as a reliable diagnostic support tool for clinicians and a self-assessment aid for patients.