Attention to FTIR: A Transformer Architecture for FTIR Spectra Classification in Oral Cancer Diagnosis - Procópio, Lucas S.; Silva, Robinson S. da; Carneiro, Murilo G.; Souza, Paulo D. (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
Fourier-transform infrared (FTIR) spectroscopy is a promising non-invasive technique for oral cancer diagnosis, whose high mortality is largely attributable to late-stage detection. This work proposes BioSpectralFormer (BSF), a Transformer-based architecture with two attention types for classification of salivary FTIR spectra. Evaluated on real spectra data under stratified 10-fold cross-validation against seven baselines, BSF achieved competitive balanced accuracy (Mean(SE,SP) = 0.67 ± 0.15) with high sensitivity (0.82 ± 0.20), operating in the same statistical tier as state-of-the-art methods. Moreover, attention map analysis corroborated established oral cancer biomarkers, including Amide I and lipid C-H stretching, suggesting biologically feature learning.