Lightweight Spectral‐ MLP for Hyperspectral Differentiation of Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma

Accurate differentiation between intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) remains challenging due to overlapping imaging features. We propose a hyperspectral imaging (HSI)–based diagnostic framework using a lightweight Spectral‐MLP network for label‐free tumor classification. Seventy‐nine liver specimens were analyzed after spectral calibration, noise‐band removal, and normalization within 450–900 nm. Spectral analysis revealed distinct differences in hemoglobin‐related bands (540–580 nm) and lipid/water absorption regions (760–850 nm), corresponding to vascular and stromal variations between ICC and HCC. The proposed Spectral‐MLP achieved 93.6% accuracy, 93.1% sensitivity, 94.0% specificity, and an AUC of 0.965, comparable to deeper models but with higher efficiency. These findings demonstrate that hyperspectral data combined with a simple neural network can effectively distinguish ICC from HCC and suggest that the lightweight Spectral‐MLP framework has the potential to provide interpretable, real‐time, and clinically deployable decision support for optical pathological diagnosis.