Characterization of Thyroid Tissue through Hyperspectral Image Analysis Using Deep Learning - Baffa, Matheus de Freitas Oliveira; Felipe, Joaquim Cezar (Companion Proceedings of the Brazilian Symposium on Computing Applied to Healthcare (SBCAS); 2025: Companion Proceedings of the 25th Brazilian Symposium on Computing Applied to Health, 2025)
Thyroid tissue classification is essential for identifying cellular anomalies in biopsies, aiding in the diagnosis of thyroid pathologies. Unlike traditional computer vision approaches, this thesis proposes a novel method based on the interaction of light with the tissue, represented by intensity spectra modeled as hyperspectral images. Six approaches were evaluated, including traditional and deep learning methods, with emphasis on one-dimensional convolutional neural networks, which achieved 97.60% accuracy. The results demonstrate superior efficiency in detecting pathologies compared to conventional analyses, highlighting the potential for advancements in modern pathology.