An Automatic Pulmonary Nodule Detection Algorithm Based on Iterative Self-organizing Data Analysis
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.
国家科技期刊平台
NSTL
万方数据
