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

• URL: http://arxiv.org/abs/2507.19843v1
• Date: Sat, 26 Jul 2025 07:36:44 GMT
• Title: Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification
• Authors: Maximilian Tschuchnig, Michael Gadermayr, Khalifa Djemal,
• Abstract summary: We present a hybrid framework combining deep convolutional features from a ResNet-50 backbone with handcrafted descriptors and transformer-based embeddings.<n>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%.
• Score: 0.21847754147782888
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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