Ravilla Dilli

Abstract Breast cancer is a major cause of cancer-related mortality among women, emphasizing the need for accurate and efficient computer-aided diagnosis. Existing deep learning approaches for mammographic analysis predominantly rely on single feature representations or conventional fusion strategies, limiting their ability to exploit complementary geometric information across multiple feature manifolds.

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