Non-invasive detection of choroidal melanoma via tear-derived protein corona on gold nanoparticles: a machine learning approach.

Additionally, Continuous Wavelet Transform (Continuous Wavelet Transform) with Mexican hat wavelet was applied to convert mass spectrometry data into 128 × 128 RGB images for deep learning analysis.

While m/z parameters showed moderate differences that didn't reach statistical significance (p = 0.082), entropy-based features demonstrated strong discriminative power.

Among traditional machine learning models, Random Forest achieved the highest accuracy (0.959 ± 0.003) and ROC AUC (0.993 ± 0.000) with remarkable computational efficiency (3.90 s per fold).