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- W4384158232 abstract "Breast cancer remains a significant public health concern and a leading cause of female mortality despite recent advances in healthcare. Experts agree that its early prognosis is a key to survivability. In this research, we proposed a deep learning architecture code-named AWFCNET. It comprised multiple segments of preprocessing techniques (color shifting & image enhancement), feature learning based on ResNeXt-101 convolutional network as a backbone with transfer and attention-aware mechanisms, and fusion classifier composed of three recurrent neural networks. The generalization capability of the pipeline produced 98.10% accuracy on a mammogram dataset using 10-fold cross-validation. Computational benchmarks revealed that it surpassed existing state-of-the-art approaches with provisions of visual interpretability via gradient maps. Thus, our framework could complement physicians’ expertise in rapid and dependable breast cancer diagnoses." @default.
- W4384158232 created "2023-07-14" @default.
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- W4384158232 date "2023-06-07" @default.
- W4384158232 modified "2023-09-26" @default.
- W4384158232 title "AWFCNET: An Attention-Aware Deep Learning Network with Fusion Classifier for Breast Cancer Classification Using Enhanced Mammograms" @default.
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- W4384158232 doi "https://doi.org/10.1109/aiiot58121.2023.10174427" @default.
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