Breast Micro-Calcification Classification: Xception and DenseNet201 Feature Fusion in Mammography

Breast cancer continues to pose a significant public health challenge with increasing prevalence. Accurate early detection is crucial for effective treatment and improved patient outcomes. This paper introduces a novel approach for breast Micro-Calcification mammography classification, distinguishing between benign and malignant cases. The proposed methodology combines image pre-processing for denoising, deep transfer learning for feature extraction, and classifier using global pooling and a Multi-Layer Perceptron. Notably, two feature extractors, Xception and DenseNet201, are combined. The study demonstrates the potential of this approach with impressive results, achieving a maximum accuracy of 98.45%.