Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI
This work addresses automated skin disease diagnosis for dermatologists, but it is incremental as it applies existing transformer methods to a medical imaging task with performance improvements.
The study tackled skin disease classification by comparing transformer-based architectures (Vision Transformers, Swin Transformers, DinoV2) with convolutional benchmarks on a 31-class dataset, achieving 96.48% test accuracy and 0.9727 F1-Score with DinoV2, a nearly 10% improvement over existing benchmarks, and validated robustness on additional datasets with slight gains.
Skin diseases affect over a third of the global population, yet their impact is often underestimated. Automating skin disease classification to assist doctors with their prognosis might be difficult. Nevertheless, due to efficient feature extraction pipelines, deep learning techniques have shown much promise for various tasks, including dermatological disease identification. This study uses a skin disease dataset with 31 classes and compares it with all versions of Vision Transformers, Swin Transformers and DivoV2. The analysis is also extended to compare with benchmark convolution-based architecture presented in the literature. Transfer learning with ImageNet1k weights on the skin disease dataset contributes to a high test accuracy of 96.48\% and an F1-Score of 0.9727 using DinoV2, which is almost a 10\% improvement over this data's current benchmark results. The performance of DinoV2 was also compared for the HAM10000 and Dermnet datasets to test the model's robustness, and the trained model overcomes the benchmark results by a slight margin in test accuracy and in F1-Score on the 23 and 7 class datasets. The results are substantiated using explainable AI frameworks like GradCAM and SHAP, which provide precise image locations to map the disease, assisting dermatologists in early detection, prompt prognosis, and treatment.