期刊
BIOMED RESEARCH INTERNATIONAL
卷 2022, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2022/8482022
关键词
-
资金
- National Natural Science Foundation of China [61702087]
- China Scholarship Council [2018GBJ002668]
This article proposes a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm to improve the accuracy of breast cancer diagnosis. The experimental results show that the LightGBM classifier performs better than other classifiers, achieving high accuracy, precision, and recall.
Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据