Hybrid Spatial and Frequency Feature Learning for Breast Cancer Analysis

Abstract

Breast cancer is one of the leading causes of cancer-related mortality among women worldwide, where early diagnosis significantly improves treatment outcomes and survival rates. Full-field digital mammography (FFDM) is widely employed for breast cancer screening; however, accurate classification of mammographic abnormalities remains challenging due to tissue density variations, low-contrast regions, and subtle microcalcification patterns. Existing deep learning approaches often face limitations in effectively capturing both structural and texture-based information required for reliable lesion classification. To address these challenges, this paper proposes a hybrid spatial and frequency feature learning framework for breast cancer analysis using mammographic images. Initially, preprocessing techniques including normalization, noise reduction, contrast enhancement, and data augmentation are applied to improve image quality and robustness. A convolutional neural network-based multiscale feature extraction module is employed to capture spatial structural information from mammograms. Simultaneously, frequency-domain representations are extracted using adaptive wavelet-based decomposition to enhance fine-grained texture and microcalcification characteristics. Furthermore, an attention-guided feature fusion mechanism is introduced to integrate complementary spatial and frequency features for improved lesion representation and classification accuracy. Finally, the fused features are processed through a fully connected classification layer to distinguish benign and malignant breast lesions. Experimental evaluation conducted on the CBIS-DDSM dataset demonstrates that the proposed framework achieves superior performance with an accuracy of 99.25%, sensitivity of 99.58%, specificity of 98.88%, F1-score of 99.57%, and AUC of 99.15%. The obtained results demonstrate the effectiveness of the proposed hybrid learning framework in improving breast cancer classification and supporting computer-aided diagnosis systems for clinical applications.