Optimizing Skin Disease Diagnosis: Harnessing Online Community Data With Contrastive Learning And Clustering Techniques

This article discusses a deep-learning framework that utilizes unannotated and coarse-labeled dermatology images from online sources for skin disease diagnosis. The model uses a three-stage classification algorithm and a filtering approach to improve generalization and reduce training costs. The approach has been tested on benchmark datasets and has the potential to revolutionize dermatology by offering a more efficient and cost-effective method for diagnosing skin diseases.