Overall model framework, divided into three main parts. The input part is a high-dimensional matrix with dimensions of 48 × 48 × 5. The feature extraction consists of five stages, with the Swin Transformer block (in blue) detailed in Sect. 3.2.2, and the patch merging (in green) described in Sect. 3.2.1. Numbers within the Swin Transformer blocks denote the repetition of stacked blocks. The third part is the head for the classification output, where the Bayesian block (see Fig. 7) maps the 8C-dimensional features to 2 or 3, corresponding to two-class and three-class classification, while also outputting the uncertainty.