计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 54-59.doi: 10.11896/jsjkx.210400211

• 智慧医疗 • 上一篇    下一篇


岳晴1, 尹健宇2, 王生生2   

  1. 1 吉林师范大学计算机学院 吉林 四平 136000
    2 吉林大学计算机科学与技术学院 长春 130000
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 王生生(wss@jlu.edu.cn)
  • 作者简介:(yueqing1994@qq.com)

Automatic Detection of Pulmonary Nodules in Low-dose CT Images Based on Improved CNN

YUE Qing1, YIN Jian-yu2, WANG Sheng-sheng2   

  1. 1 School of Computer Science,Jilin Normal University,Siping,Jilin136000,China
    2 College of Computer Science and Technology,Jilin University,Changchun 130000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:YUE Qing,born in 1994,master.Her main research interests include medical image analysis and machine learning.
    WANG Sheng-sheng,born in 1974,Ph.D,professor.His main research interests include the areas of computer vision,deep learning and data mining.

摘要: 随着空气污染日益严重,肺癌已成为发病率和死亡率增长速度最快的恶性肿瘤之一,严重危害人们的生命和健康。肺癌早期主要表现为肺结节的形式,如果在肺癌早期能够及时发现并治疗,将能够提高肺癌的治疗效果。低剂量螺旋CT具有采集速度快、成本低、辐射低的特点,因此被大量应用于对肺结节的诊断。目前,CT图像的诊断多采用传统的人工诊断方式与CAD系统诊断的方式,但这两种方式存在精确性低、泛化性差的缺点。针对上述问题,文中以医学辅助诊断领域中的肺结节检测问题为研究对象,提出了一种基于改进CNN的低剂量CT图像的肺结节自动检测算法。首先,对CT图像进行预处理,提取肺实质;其次,对cascade-rcnn候选结节筛选网络进行改进,以提取更高质量的目标;然后,提出了改进3D CNN的假阳性减少网络,提高了结节分类的准确性;最后,在LUNA16数据集上进行了实验,结果表明,与现有算法相比,所提算法在检测准确率上有所提升。

关键词: 3D CNN, cascade-rcnn, LUNA16, 肺结节检测, 假阳性降低

Abstract: With air pollution getting worse and worse,lung cancer has become one of the malignant tumors with the fastest increasing morbidity and mortality rate,which seriously endangers people's life and health.The early stage of lung cancer is mainly in the form of pulmonary nodules.If the early stage of lung cancer can be detected and treated in time,the treatment effect of lung cancer will be improved.Low-dose spiral CT is widely used in the diagnosis of pulmonary nodules because of its characteristics of fast acquisition speed,low cost and low radiation.At present,CT image diagnosis mostly adopts the traditional manual diagnosis and CAD system diagnosis,but these two methods have the disadvantages of low accuracy and poor generalization.In view of the above problems,this paper takes the detection of pulmonary nodules in the field of medical assisted diagnosis as the research object,and proposes an improved low-dose CT image automatic detection algorithm for pulmonary nodules based on CNN.Firstly,the CT images are preprocessed to extract the lung parenchyma.Secondly,the cascade-rcnn candidate nodule screening network is improved to extract higher quality targets.Thirdly,an improved 3D CNN false positive reduction network is proposed to improve the accuracy of nodular classification.Finally,experiments are carried out on Luna16 dataset.Compared with existing algorithms,the detection accuracy of the proposed algorithm is improved.

Key words: 3D CNN, Cascade-RCNN, False positive reduction, LUNA16, Pulmonary nodule detection


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