Cross-Validation Deep Learning for Breast Cancer Detection Using DMR-IR Infrared Images - Melo, Renata dos Santos; Fernandes, Henrique Coelho; Backes, André Ricardo (Revista de Informática Teórica e Aplicada, 2026)
Breast cancer detection is a global health priority. While traditional methods have limitations, infrared thermography offers a promising, non-invasive alternative by detecting subtle thermal changes that can indicate tumors. This paper assessed five pre-trained Convolutional Neural Networks (CNNs) for breast cancer detection using DMR-IR thermal images, employing a 5-fold cross-validation. Among the tested models, ResNet50 achieved the best overall performance, with the highest average accuracy (92.79%), precision (95.00%), specificity (98.67%), sensitivity (72.00%), and F1-score (79.43%). The model was trained using raw thermal images from three anatomical views (frontal, lateral 90°, and lateral 45°), totaling five images per patient, an approach still uncommon in the literature. These results highlight ResNet50’s strong potential for reliable and clinically applicable breast cancer detection using thermography.