Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification
This work addresses the challenge of distinguishing benign from malignant tissue in mammograms for clinical decision support, presenting an incremental improvement over existing methods.
The paper tackled automated breast cancer classification from mammography by combining deep convolutional features with handcrafted descriptors and transformer-based embeddings, achieving an AUC of 79.6% and a peak recall of 80.5% on the CBIS-DDSM dataset.
Automated breast cancer classification from mammography remains a significant challenge due to subtle distinctions between benign and malignant tissue. In this work, we present a hybrid framework combining deep convolutional features from a ResNet-50 backbone with handcrafted descriptors and transformer-based embeddings. Using the CBIS-DDSM dataset, we benchmark our ResNet-50 baseline (AUC: 78.1%) and demonstrate that fusing handcrafted features with deep ResNet-50 and DINOv2 features improves AUC to 79.6% (setup d1), with a peak recall of 80.5% (setup d1) and highest F1 score of 67.4% (setup d1). Our experiments show that handcrafted features not only complement deep representations but also enhance performance beyond transformer-based embeddings. This hybrid fusion approach achieves results comparable to state-of-the-art methods while maintaining architectural simplicity and computational efficiency, making it a practical and effective solution for clinical decision support.