HSI-MSSAF net: A dual-stream network for nasal tumor tissue diagnosis using hyperspectral spectral-spatial features

Abstract

Accurate differentiation of benign and malignant nasal cavity lesions is clinically critical due to their lack of distinct morphological specificity. Hyperspectral imaging (HSI), emerging as a novel modality, deciphers spatial-spectral multidimensional signatures of pathological tissues, thereby delivering novel data dimensions for diagnostic precision beyond conventional histomorphological limitations. This study employs HSI technology and a multi-scale spatial-spectral attention fusion network (HSI-MSSAF net), which combines residual networks, Transformer network architecture, and multi-scale attention mechanisms. This approach efficiently extracts and integrates spatial-spectral features from different scales and channels.Experimental results show that the proposed method achieves remarkable performance in differentiating benign and malignant nasal tumors, with a classification accuracy of 91.8%, precision of 0.91, recall of 0.92, F1 score of 0.92, AUC of 0.98, and Matthews correlation coefficient of 0.84. The results indicate that the proposed model effectively leverages sample data to learn comprehensive joint feature representations. The novel methodology introduced herein offers a complementary strategy that may mitigate certain limitations inherent to conventional medical diagnostic techniques, thereby underscoring the potential of high-precision diagnostic approaches in facilitating the classification and prognostic evaluation of complex nasal tumors. By establishing a dedicated hyperspectral nasal tumor database and implementing advanced network architectures, this approach demonstrates potential for clinical integration, contingent upon further in vivo validation.