Precise delineation of glioma margins in hyperspectral images using cross-channel spectral feature fusion.

Hyperspectral imaging, as a novel non-contact, non-ionizing radiation imaging technique, can acquire abundant spectral-spatial information without damaging tissue. Addressing the critical challenge of precisely delineating boundaries during brain glioma surgery, this study proposes a new hyperspectral-assisted diagnostic and classification method for brain gliomas based on cross-channel fusion feature extraction. The method acquires tissue spectral-spatial information using hyperspectral imaging, employs a spatial-spectral fusion channel feature extraction algorithm, and designs a multi-branch feature extraction network to achieve precise discrimination between tumor and normal tissue. The network uses a multi-scale feature extraction architecture: the main branch extracts deep semantic features via composite convolutional units, while a side branch preserves the original spectral-spatial information and integrates features through residual connections, thereby substantially enhancing the model's representational capacity and robustness. The advantages of this method lie in its balance of local detail and global semantics; by complementing information across channels, it effectively strengthens feature discriminability, while also offering good generalization and interpretability. Experimental results show a classification accuracy of 97% on the validation set, a maximum accuracy of 96% on an external test set, and an average accuracy of 90%. ROC curve analysis indicates stable model performance, with AUC values up to 0.99 and an average of 0.95, significantly outperforming traditional methods. Further analysis confirms that the model effectively captures discriminative features in key spectral bands (e.g., 590-610 nm and 650-700 nm), which are highly related to hemoglobin absorption characteristics, thus providing a clear biophysical explanation for the classification results. This study not only validates the effectiveness of hyperspectral imaging combined with deep learning for brain glioma recognition, but the proposed multi-branch architecture also offers a new technical pathway for medical hyperspectral image analysis.