Enhanced Breast Cancer Detection: A Transfer Learning Framework with Auxiliary Classification for Mammographic Image Analysis
Van-Thanh Hoang, Vu Huu Dao, Tu Minh Phuong, Kang-Hyun Jo · 2025
Breast cancer remains one of the most prevalent malignancies affecting women worldwide, with over 2.3 million new cases diagnosed annually. While conventional diagnostic methods like mammography have reduced mortality rates, artificial intelligence offers opportunities to further enhance detection accuracy. In this paper, we present a comprehensive framework for breast cancer identification using deep learning techniques applied to mammographic images. Our approach leverages transfer learning from ImageNet pretrained models and incorporates an auxiliary classification mechanism to improve diagnostic performance. We evaluated multiple state-of-the-art convolutional neural network architectures, including MobileNetV3, ResNet50, ConvNeXtV2-nano, ResNeXt50, MobileViT-small, and EfficientNet-B3, trained on four publicly available datasets (BMCD, CDD-CESM, CMMD, and MiniDDSM). Performance was assessed on separate external test datasets (VinDr and RSNA) to simulate real-world clinical deployment scenarios. Our experimental results demonstrate that EfficientNet-B3 with auxiliary classification achieved superior performance across most metrics, with an accuracy of 86.14%, F1-score of 85.80%, and AUC of 90.39% on validation data. We further introduce the Probabilistic F1-score as a clinically relevant evaluation metric that accounts for prediction confidence rather than binary decisions alone. The proposed framework, available as an open-source implementation, offers a promising approach for enhancing breast cancer detection while providing insights into the challenges of deploying AI systems in diverse clinical environments. We released our codebase at: https://github.com/thanhhnvnqb/mmbreast