计算机与数字工程2024，Vol.52Issue(5)：1336-1340,1358,6.DOI:10.3969/j.issn.1672-9722.2024.05.013
融合迭代自组织数据分析的肺结节自动检测算法
An Automatic Pulmonary Nodule Detection Algorithm Based on Iterative Self-organizing Data Analysis
摘要
Abstract
Early diagnosis of pulmonary nodules can provide possibility for early detection of lung cancer.Computed tomogra-phy(CT)provides a convenient method for the diagnosis of pulmonary nodules.In order to assist doctors to read lung nodules in CT,an automatic detection algorithm based on convolutional neural network(CNN)is proposed in this paper.Based on the Faster R-CNN method,an iterative self-organizing data analysis techniques algorithm(ISODATA)is firstly presented.ISODATA,based on the clustering learning of the input lung CT images,automatically generates anchor frame parameters that are more in line with the morphological characteristics of pulmonary nodules and then guides the regional candidate network to generate candidate frames that are much closer to the real size of the nodules,so as to reduce the false positive results while improving the accuracy of pulmo-nary nodules detection.Secondly,Focal Loss is used for the classification,and relatively high weights are assigned to the lung nodu-lar samples that are difficult to identify so as to enhance the learning of these samples,and further improve the classification ability of the algorithm.The experimental results have demonstrated that the proposed algorithm on the open LIDC-IDRI dataset is superior to the state-of-the-art algorithms.It implies great potential in correctly distinguishing the authenticity of pulmonary nodules.关键词
肺结节检测/Faster R-CNN/聚类学习/损失函数/卷积神经网络Key words
pulmonary nodule detection/Faster R-CNN/clustering learning/loss function/convolutional neural network分类
信息技术与安全科学引用本文复制引用
张浩,解艺旋,杨晓楠,岳晨阳,马明,夏立坤..融合迭代自组织数据分析的肺结节自动检测算法[J].计算机与数字工程,2024,52(5):1336-1340,1358,6.基金项目
国家自然科学基金项目(编号:61572076) （编号:61572076）
北京自然科学基金项目(编号:4202011) （编号:4202011）
首都师范大学交叉科学研究院引导研发课题(编号:JCKXYJY2019018)资助. （编号:JCKXYJY2019018）
