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

TL;DR

This paper presents a deep learning model based on Swin Transformer for skin disease diagnosis, achieving 87.71% accuracy on ISIC2019, aiming to assist clinicians and patients amid dermatologist shortages.

Contribution

The study introduces an imbalance-aware deep learning approach with optimized preprocessing and augmentation for skin disease classification, utilizing a Swin Transformer architecture.

Findings

Achieved 87.71% accuracy on ISIC2019 dataset.

Effective feature extraction with pretraining on skin datasets.

Potential as a diagnostic support tool for clinicians and patients.

Abstract

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.