Application of Convolutional Neural Networks in Breast Cancer Detection: Hybrid and Attention-based Models

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Hybrid models achieve over 98% classification accuracy using datasets such as MIAS, enhancing early detection.
Attention-based strategies like CBAM-Xception show an area under curve of 0.97, crucial for differentiating lesions.
Automation of feature extraction from medical images is key to improving breast cancer diagnosis and prognosis.
Challenges remain in interpretability and data heterogeneity, highlighting the need for integrated expert systems.

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Systematic review reveals hybrid and attention-based CNNs improve diagnosis and classification accuracy in breast cancer detection.

J. T. L. Wang (2025) studied this question.

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