WDCViT: Enhancing Monkeypox Prediction via Lightweight Vision Transformer with Window Attention and Dilated Convolutions

Monkeypox was declared a public health emergency of international concern by WHO in July 2022 due to the unprecedented global spread of the disease outside of previously endemic countries in Africa. Previously proposed classification models based mainly on pre-trained CNNs are ineffective in detecting Monkeypox. In this paper, a novel model is presented for the Monkeypox virus detection hybrid window attention and depthwise asymmetric dilation convolution. This study's novelty lies in the proposed lightweight and robust multi-stage model, utilizing depthwise dilation convolution and pointwise convolution in the two first stages with a large spatial dimension. Window attention combination with depthwise asymmetric dilation convolution is applied at the two last stages to reduce model complexity. We have evaluated the proposed approach with ViT, Swin Transformer, and MaxViT, DenseNet201, and ResNet50 on the public dataset of “Monkeypox Skin Images Dataset” (MSID) on Mendeley. The model performance was evaluated using metrics such as accuracy, recall, precision, specificity, and F1-score. The proposed method achieves the best results with an average of 99.30%, 98.65%, 98.60%, 99.56%, and 98.60% in accuracy, precision, recall, and specificity, F1- score, respectively. Additionally, the proposed model has only 19M parameters, which is smaller than MaxViT and Swin Transformer.