Classification of liver tissue pathological changes via optical biopsy based on refractive index sensing.

Optical biopsy enables minimally invasive, quantitative tissue assessment, yet clinically useful implementations require rapid and objective decision-making from compact sensors. We present a refractive-index (RI) driven classification framework based on reflection spectra acquired with an extrinsic fiber-optic Fabry-Pérot interferometric cavity (280 μm) over the biologically relevant RI range 1.33-1.42. Three proxy classes ("healthy", "HCC-like", "metastatic") were defined using literature-guided RI windows for liver tissue, and measurements were performed on certified reference liquids. As a physics-only reference, RI was estimated analytically from fringe periodicity in the wavenumber domain, achieving 0.70 accuracy and 0.48 macro-F1. To enhance discrimination, we engineered 62 spectral descriptors capturing fringe spacing (RI-related), fringe visibility, and spectral-shape cues, and trained tree-ensemble and SVM models together with an interpretable GA-optimized fuzzy expert system. On a held-out test set, tree ensembles reached macro-F1 = 1.00, while SVM and the fuzzy system achieved 0.96 and 0.97, respectively. Feature attribution identified RI as the dominant discriminative signal, with visibility-related metrics improving robustness near the HCC-like boundary. These results demonstrate that ML-augmented fiber-optic interferometry can deliver accurate and explainable diagnostic signatures, supporting the translational potential of RI-based optical biopsy.