Breast Cancer Segmentation using Attention-based Convolutional Network and Explainable AI
This work addresses the challenge of high false positives and negatives in mammography interpretation for breast cancer detection, offering a domain-specific improvement.
The paper tackles the problem of early breast cancer detection by developing an attention-based convolutional neural network for segmentation using thermography images, achieving increased speed and precision compared to existing deep learning frameworks.
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.