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doi: https://doi.org/10.53941/aim.2024.100005
Wang, Y., Zhao, Q., Zhang, B., Tian, D., Zhang, R., & Zhong, W. A Comparative Study of Deep Learning in Breast Ultrasound Lesion Detection: From Two-Stage to One-Stage, from Anchor-Based to Anchor-Free. AI Medicine. 2024, 1(1), 5. doi: https://doi.org/10.53941/aim.2024.100005
Volume 1, lssue 1, Dec 2024
Copyright (c) 2024 by the authors.
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Article

A Comparative Study of Deep Learning in Breast Ultrasound Lesion Detection: From Two-Stage to One-Stage, from Anchor-Based to Anchor-Free

Yu Wang 1, Qi Zhao 1, Baihua Zhang 2, Dingcheng Tian 1, Ruyi Zhang 1 and Wan Zhong 3,∗

1 College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110024 , China

2 Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo 315000, China

3 General Hospital of Northern Theater Command, Shenyang 110024, China

∗ Correspondence: wzhong_88@163.com

Received: 16 July 2024; Revised: 26 August 2024; Accepted: 27 August 2024; Published: 4 September 2024

 

Abstract: Breast cancer is one of the most common tumors among women in the world, and its early screening is crucial to improve the survival rate of patients. Breast ultrasound, with the characteristics of non radiation, real-time imaging and easy operation, has become a common method for breast cancer detection. However, this method has some problems, such as low imaging quality and strong subjectivity of diagnosis results, which affect the accurate diagnosis of breast cancer. With the ongoing advancement of deep learning technology, intelligent breast cancer detection systems have effectively overcome these challenges, enhancing diagnostic accuracy and efficiency. This study uses nine popular deep learning object detection networks (including two-stage, one-stage, anchor-based, and anchor-free networks) for the detection of breast lesions and compares the results of these methods. The experiments show that the anchor-based Single Shot MultiBox Detector (SSD) network excels in overall performance, while the anchor-free Fully Convolutional One-stage Object Detector (FCOS) exhibits the best generalization ability. Moreover, the results also indicate that, in the context of breast lesion detection, anchor-based networks generally outperform anchor-free networks.

Keywords:

deep learning breast ultrasound image breast cancer breast lesion detection object detection

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