Hybrid feature integration for enhanced atopic dermatitis diagnosis using multi-ResNet transformer models

We proposed a “hybrid fusion multi-ResNet transformer” (HF-MRT) model to improve the diagnosis of atopic dermatitis (AD) by using both hand-crafted and deep learning features. The Vietnam Atopic Dermatitis (VNAD) dataset, consisting of 7861 images of AD and non-AD cases, was utilized. Preprocessing and data augmentation techniques were applied to enhance model performance and generalization. Experiments were conducted across three feature scenarios: the first scenario focused on hand-crafted features, including a histogram of oriented gradients, local binary pattern, and color coherence vector; the second scenario involved deep learning features extracted from ResNet models (ResNet-50, ResNet-101, and ResNet-152); and the third scenario combined both hand-crafted and deep learning features. In all scenarios, high sensitivity rates were achieved by the HF-MRT model, reaching 91.4% in scenario 1, 93% in scenario 2, and 93% in scenario 3, providing reliable identification of most AD cases. The specificity rates, however, differed: 40.2% in scenario 1, 56.2% in scenario 2, and 60.9% in scenario 3. Scenario 3, with a sensitivity of 93% and a specificity of 60.9%, demonstrated diagnostic potential for detecting AD. Nevertheless, improvements in specificity remain necessary to enhance clinical applicability, ensuring accurate discrimination between AD and non-AD cases.