ConvNeXt for Breast Cancer Classification in Infrared Images - Louzada, Víctor Emmanuel Olmi; Baffa, Matheus de Freitas Oliveira (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
Breast cancer is a major public health concern, highlighting the need for accurate and accessible diagnostic tools. Infrared imaging offers a non-invasive alternative for detecting physiological abnormalities associated with tumor development. In this study, we evaluate the use of ConvNeXt architectures for breast cancer classification based on static infrared images. A patient-level stratified cross-validation protocol was adopted to ensure robust and unbiased assessment. Among the evaluated models, ConvNeXt-Large achieved the best performance, reaching an accuracy of 90.43% and an AUC of 98.29%. These results demonstrate the potential of modern deep learning architectures for supporting breast cancer detection using infrared imaging.