Distinguishing Low‐Grade Chondrosarcoma and Osteochondroma Using Visible–Near Infrared Hyperspectral Spectral Characteristics

ABSTRACT Purpose To evaluate visible–near‐infrared hyperspectral imaging (400–1000 nm) combined with a lightweight deep‐learning network for differentiating low‐grade chondrosarcoma (LGC) from osteochondroma (OC). Methods Spectra were reflectance‐calibrated, Savitzky–Golay smoothed, and cropped to 420–850 nm. Multi‐level features—single‐band reflectance, key band ratios, and PCA components from biologically informative regions were extracted. We developed ChondroSpecNet , a 1D ‐ CNN coupling multi‐scale convolutions with a residual squeeze‐and‐excitation block for end‐to‐end classification. Results ChondroSpecNet yielded AUC 0.92 with 87% accuracy in training, and AUC 0.83 with 86% accuracy on an independent test set. Contribution analysis identified visible‐band troughs—especially 472, 467, and 501 nm—as most discriminative by absolute differences and normalized ratios. Conclusion Hyperspectral microscopy plus a compact network enables accurate, efficient LGC–OC discrimination, offering real‐time deployability with robust performance and practical scalability for cartilaginous‐tumor diagnosis in clinical workflows.