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.

本研究提出了一种基于注意力机制的卷积神经网络，结合了高分辨率红外热成像摄像机和人工智能，用于乳腺癌检测和分类。我们采用可解释性人工智能技术，通过梯度加权类激活图来分析深度学习架构的偏差和问题。结果表明，与现有深度学习框架相比，我们提出的框架具有明显的优越性。

基于注意力卷积网络和可解释人工智能的乳腺癌分割