GFNet: A Deep Learning Framework for Breast Mass Detection

• Published in: Electronics (Basel) Vol. 12; no. 7; p. 1583
• Main Authors: Yu, Xiang, Zhu, Ziquan, Alon, Yoav, Guttery, David S, Zhang, Yudong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Accuracy; Breast cancer; Candidates; Classification; Datasets; Decision trees; Diagnosis; Feature extraction; Image processing; Machine learning; Mammography; Medical screening; Methods; Modules; Morphology; Performance enhancement; Reduction; Texture; United Kingdom
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12071583

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.