  Breast cancer (BC) remains a significant health threat, with no long-term
cure currently available. Early detection is crucial, yet mammography
interpretation is hindered by high false positives and negatives. With BC
incidence projected to surpass lung cancer, improving early detection methods
is vital. Thermography, using high-resolution infrared cameras, offers promise,
especially when combined with artificial intelligence (AI). This work presents
an attention-based convolutional neural network for segmentation, providing
increased speed and precision in BC detection and classification. The system
enhances images and performs cancer segmentation with explainable AI. We
propose a transformer-attention-based convolutional architecture (UNet) for
fault identification and employ Gradient-weighted Class Activation Mapping
(Grad-CAM) to analyze areas of bias and weakness in the UNet architecture with
IRT images. The superiority of our proposed framework is confirmed when
compared with existing deep learning frameworks.
