A data-driven pipeline reduces EIS measurements by 99% and achieves 80% accuracy with AUC 0.90 for healthy vs. cancer classification plus AUCs above 0.82 in multi-class oral lesion tasks using leave-one-patient-group-out validation.
Use of electrical impedance spectroscopy to detect malig- nant and potentially malignant oral lesions
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Optimizing In Vivo Oral Lesion Classification from Electrical Impedance Spectroscopy Using Data-driven Approaches
A data-driven pipeline reduces EIS measurements by 99% and achieves 80% accuracy with AUC 0.90 for healthy vs. cancer classification plus AUCs above 0.82 in multi-class oral lesion tasks using leave-one-patient-group-out validation.