MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework

Zhong, Shengzhou; Tu, Chao; Dong, Xiuyu; Feng, Qianjin; Chen, Wufan

doi:10.1016/j.cmpb.2023.107346

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…In this section, we delve into the comprehensive performance evaluation of our YOLOv5n model for breast cancer detection. Inspiring from [21]- [23], we employ key performance metrics such as precision, recall, mAP, and F1score to assess the model's accuracy. Precision estimates the percentage of accurately predicted positive instances among all anticipated positives, whereas recall evaluates the model's capacity to accurately identify every positive case.…”

Section: B Performance Evaluationmentioning

confidence: 99%

A Single Stage Detector for Breast Cancer Detection on Digital Mammogram

Xu,

Jia,

Zhang

2024

IJACSA

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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 deeplearning 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.

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Section: B Performance Evaluationmentioning

confidence: 99%