ScabAtlas: A Comprehensive Medical Image Dataset for Scabies Detection

Scabies, a neglected tropical disease caused by Sarcoptes scabiei var. hominis, remains a worldwide public health issue, particularly in low and middle-income countries, where overcrowding and poor hygiene practices increase the risk of rapid transmission. Therefore, early and accurate diagnosis is essential for preventing outbreaks, yet clinical identification is often proven to be difficult for overlapping syndromes with other dermatological conditions or diseases. In this regard, deep learning-based image analysis offers a promising solution for scalable and automated scabies detection. In this study, we constructed a dedicated Scabies Image Dataset, ScabAtlas, sourced from DernBET, medical camps, and hospital records from Bangladesh, and further augmented it. Using this dataset, we evaluated seven ImageNet-pretrained convolutional neural network architectures, including ResNet18, ResNet50, MobileNetV3 (Small and Large), EfficientNetB0, ConvNeXt-Tiny, and VGG16-BN, using a five-fold cross-validation scheme. Experimental results exhibited that ResNet50 achieved the best overall performance with an accuracy of 88.7% (\u00b10.02) and a macro-F1 score of 0.91, outperforming other models. Grad-CAM visualizations confirmed that the network consistently focused on lesion-affected regions, while embedding-based image retrieval highlighted class-consistent feature representations. We have also developed a mobile application based on the best-performing model that can identify scabies skin lesions, aggregating symptoms with the model's learned features from the images.