Improving YOLO-based breast mass detection with transfer learning pretraining on the OPTIMAM Mammography Image Database.

Journal: Computers in biology and medicine
Published Date: Jul 1, 2025

Abstract

BACKGROUND AND OBJECTIVES: Early detection of breast cancer through mammography significantly improves survival rates. However, high false positive and false negative rates remain a challenge. Deep learning-based computer-aided diagnosis systems can assist in lesion detection, but their performance is often limited by the availability of labeled clinical data. This study systematically evaluated the effectiveness of transfer learning, image preprocessing techniques, and the latest You Only Look Once (YOLO) model (v9) for optimizing breast mass detection models on small proprietary datasets.

Authors

  • Pei-Shan Ho
    Department of Engineering and System Science, National Tsing-Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan. Electronic address: aeojflower@gmail.com.
  • Hui-Yu Tsai
    Institute of Nuclear Engineering and Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan. Electronic address: huiyutsai@mx.nthu.edu.tw.
  • Ivy Liu
    Institute of Nuclear Engineering and Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan. Electronic address: ivyliu0407@gmail.com.
  • Yuan-Yu Lee
    Institute of Nuclear Engineering and Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan. Electronic address: lee870304@gmail.com.
  • Si-Wa Chan
    Department of Medical Imaging, Taichung Veterans General Hospital, No. 1650, Taiwan Boulevard Sect. 4, Taichung, 407219, Taiwan; Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, No. 666, Buzih Road, Beitun District, Taichung, 40601, Taiwan; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, No. 145, Xinda Road, South Dist., Taichung, 402202, Taiwan. Electronic address: chan.siwa@gmail.com.

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