Su Ying, Li Dan, Chen Xiaodong
Department of Nursing, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110000, China.
Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110000, China.
Comput Methods Programs Biomed. 2021 Mar;200:105866. doi: 10.1016/j.cmpb.2020.105866. Epub 2020 Nov 22.
Lung cancer is a worldwide high-risk disease, and lung nodules are the main manifestation of early lung cancer. Automatic detection of lung nodules reduces the workload of radiologists, the rate of misdiagnosis and missed diagnosis. For this purpose, we propose a Faster R-CNN algorithm for the detection of these lung nodules.
Faster R-CNN algorithm can detect lung nodules, and the training set is used to prove the feasibility of this technique. In theory, parameter optimization can improve network structure, as well as detection accuracy.
Through experiments, the best parameters are that the basic learning rate is 0.001, step size is 70,000, attenuation coefficient is 0.1, the value of Dropout is 0.5, and the value of Batch Size is 64. Compared with other networks for detecting lung nodules, the optimized and improved algorithm proposed in this paper generally improves detection accuracy by more than 20% when compared with the other traditional algorithms.
Our experimental results have proved that the method of detecting lung nodules based on Faster R-CNN algorithm has good accuracy and therefore, presents potential clinical value in lung disease diagnosis. This method can further assist radiologists, and also for researchers in the design and development of the detection system for lung nodules.
肺癌是一种全球高危疾病，肺结节是早期肺癌的主要表现形式。自动检测肺结节可减轻放射科医生的工作量，降低误诊和漏诊率。为此，我们提出一种用于检测这些肺结节的更快区域卷积神经网络（Faster R-CNN）算法。
Faster R-CNN算法可检测肺结节，利用训练集来证明该技术的可行性。理论上，参数优化可改进网络结构以及检测精度。
通过实验，最佳参数为基础学习率0.001、步长70000、衰减系数0.1、随机失活（Dropout）值0.5以及批量大小（Batch Size）值64。与其他用于检测肺结节的网络相比，本文提出的优化改进算法与其他传统算法相比，检测精度普遍提高了20%以上。
我们的实验结果证明，基于Faster R-CNN算法检测肺结节的方法具有良好的准确性，因此在肺部疾病诊断中具有潜在的临床价值。该方法可进一步辅助放射科医生，也可供研究人员用于肺结节检测系统的设计与开发。
