ROI Segmentation in Thyroid Thermography via Deep Learning - Silveira, Alessandro C. B. da; Pinto, Sílvia C. D. (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
Thyroid cancer is the most frequent endocrine tumor in the world, and its traditional diagnosis depends on invasive methods such as ultrasonography and fine-needle aspiration biopsy (FNAB), which are subject to limitations. Infrared thermography emerges as a non-invasive and accessible alternative examination that records the thermal increase caused by tumor hypermetabolism. Thus, this study proposes an automatic approach through the adaptation of U-Net to obtain segmentation of the region of interest in thermographic images of the thyroid from the Visual Lab Thyroid Database. It was concluded that the proposed model achieved good performance (Dice: 0.9066; IoU: 0.8312; Accuracy: 0.8913), but still faces challenges in the delineation of fine contours due to the reduction of convolutional blocks in the encoder and loss of resolution. The results highlight the potential of the solution to automate initial screening, suggesting as next steps the use of K-fold cross-validation and morphological operations to refine the obtained masks.