Improved YOLOv8 Model for Enhanced Small-Sized Breast Mass Detection on Magnetic Resonance Imaging

The early detection of breast cancer is critically important for prompt treatment and rescuing lives. However, the accuracy of small-sized breast masses’ early detection in various algorithms remains unsatisfactory, as the small-sized masses often exhibit subtle features, contain blurry boundaries, and may overlap with other parts in crowded magnetic resonance imaging (MRI) images. This research proposes an improved object detection model based on You Only Look Once (YOLO) v8 to enhance small-sized breast mass detection on MRI. A feature fusion method called the Bidirectional Feature Pyramid Network (BiFPN) and an attention mechanism called the Convolutional Block Attention Module (CBAM) are integrated into the YOLOv8 architecture. The improved YOLOv8 model, equipped with CBAM and BiFPN and hyperparameter tuning, achieved the best performance with a precision of 95.7%, a mAP50 of 91.2%, a recall of 84.3%, and the shortest inference time of 3.4ms per image. The proposed improved Yolov8 model outperformed the baseline model with improvements in precision, mAP50, and recall of 6%, 3.9%, and 2.1%, respectively. The inference time per image is reduced by 1.4ms as well. It is hoped that the proposed model could be applied in the clinical field to increase the early detection rate of breast cancer and the life expectancy of women in the world.

@article{Wu2025,
  title     = {Improved YOLOv8 Model for Enhanced Small-Sized Breast Mass Detection on Magnetic Resonance Imaging},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {7},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Feiyan Wu and Chia Yean Lim and Sau Loong Ang and Jiaxin Zheng},
  doi       = {10.14569/IJACSA.2025.0160714},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160714}
}