GFNet: A Deep Learning Framework for Breast Mass Detection
journal contributionposted on 2023-05-25, 09:13 authored by Xiang Yu, Ziquan Zhu, Yoav Alon, David S Guttery, Yudong Zhang
Background: Breast mass is one of the main symptoms of breast cancer. Effective and accurate detection of breast masses at an early stage would be of great value for clinical breast cancer analysis. Methods: We developed a novel mass detection framework named GFNet. The GFNet is comprised of three modules, including patch extraction, feature extraction, and mass detection. The developed breast mass detection framework is of high robustness and generality that can be self-adapted to images collected by different imaging devices. The patch-based detection is deployed to improve performance. A novel feature extraction technique based on gradient field convergence features (GFCF) is proposed to enhance the information of breast mass and, therefore, provide useful information for the following patch extraction module. A novel false positives reduction method is designed by combining the texture and morphological features in breast mass patch. This is the first attempt at fusing morphological and texture features for breast mass false positive reduction. Results: Compared to other state-of-the-art methods, the proposed GFNet showed the best performance on CBIS-DDSM and INbreast with an accuracy of 0.90 at 2.91 false positive per image (FPI) and 0.99 at only 0.97 FPI, respectively. Conclusions: The GFNet is an effective tool for detecting breast mass.
Driving innovation in precision medicine through translational life sciences research at the University of Leicester
Medical Research CouncilFind out more...
Royal Society, UK (RP202G0230)
Accelerator Award (round 1)
British Heart FoundationFind out more...
the Hope Foundation for Cancer Research, UK (RM60G0680)
GCRF, UK (P202PF11)
Sino-UK Industrial Fund, UK (RP202G0289)
LIAS, UK (P202ED10, P202RE969)
the Data Science Enhancement Fund, UK (P202RE237)
the Fight for Sight, UK (24NN201)
the Sino-UK Education Fund, UK (OP202006)
BBSRC, UK (RM32G0178B8)
Author affiliationSchool of Computing and Mathematical Sciences, University of Leicester
- VoR (Version of Record)