Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification

TL;DR

This paper introduces a hybrid deep learning and handcrafted feature fusion framework for mammographic breast cancer classification, improving accuracy and efficiency over traditional methods using a combination of deep convolutional, handcrafted, and transformer features.

Contribution

The study presents a novel hybrid fusion approach combining deep, handcrafted, and transformer features, demonstrating improved classification performance on mammography data.

Findings

Fusion of handcrafted and deep features improves AUC to 79.6%

Hybrid approach achieves high recall and F1 scores

Method maintains simplicity and computational efficiency

Abstract

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.