Abstract: Medical image processing plays a pivotal role in modern healthcare, and the early detection of breast cancer in digital mammograms. Several methods have been explored in the literature to improve breast cancer detection, with deep-learning approaches emerging as particularly promising due to their ability to provide accurate results. However, a persistent research challenge in deep learning-based breast cancer detection lies in addressing the historically low accuracy rates observed in previous studies. This paper presents a novel deep-learning model utilizing a single-stage detector based on the YOLOv5 algorithm, designed specifically to tackle the issue of low accuracy in breast cancer detection. The proposed method involves the generation of a custom dataset and subsequent training, validation, and testing phases to evaluate the model's performance rigorously. Experimental results and comprehensive performance evaluations demonstrate that the proposed method achieves remarkable accuracy, marking a significant advancement in breast cancer detection through extensive experiments and rigorous performance analysis.

Li Xu, Nan Jia and Mingmin Zhang, “A Single Stage Detector for Breast Cancer Detection on Digital Mammogram” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150312

@article{Xu2024,
title = {A Single Stage Detector for Breast Cancer Detection on Digital Mammogram},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150312},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150312},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Li Xu and Nan Jia and Mingmin Zhang}
}