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- W4378468577 abstract "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." @default.
- W4378468577 created "2023-05-27" @default.
- W4378468577 creator A5002105167 @default.
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- W4378468577 date "2023-05-22" @default.
- W4378468577 modified "2023-09-25" @default.
- W4378468577 title "Breast Cancer Segmentation using Attention-based Convolutional Network and Explainable AI" @default.
- W4378468577 doi "https://doi.org/10.48550/arxiv.2305.14389" @default.
- W4378468577 hasPublicationYear "2023" @default.
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