3D Swin Transformer With Multi-Scale Dilated Convolution for White Blood Cell Hyperspectral Image Classification

White blood cell (WBC) classification plays a crucial role in clinical diagnosis. However, traditional microscopic examination and existing deep learning methods primarily focus on spatial structures, resulting in the insufficient utilization of spectral information. In this study, we apply hyperspectral imaging technology and propose a 3D Swin Transformer network (SwinMDC) based on multi-scale dilated convolutions for WBC hyperspectral image classification. The network employs a 3D multi-scale dilated convolution feature extractor as its initial embedding layer, thereby enhancing low-level representations and alleviating receptive field limitations. By integrating a 3D window-based attention mechanism, the network constructs a hierarchical Transformer encoder to capture long-range dependencies. On a three-class white blood cell microscopic hyperspectral dataset, SwinMDC achieved an overall classification accuracy of 99.07%, demonstrating its potential value for clinical white blood cell analysis.