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首页> 外文期刊>Journal of Medical Imaging and Health Informatics >Research on CT Scan Image of Lung Cancer Based on Deep Learning Method in Artificial Intelligence Field
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Research on CT Scan Image of Lung Cancer Based on Deep Learning Method in Artificial Intelligence Field

机译：基于人工智能领域深层学习方法的肺癌CT扫描图像研究

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摘要

Cancer is one of the diseases with high mortality in the 21st century, and lung cancer ranks first in all cancer morbidity and mortality. In recent years, with the rise of big data and artificial intelligence, lung cancer-assisted diagnosis based on deep learning has gradually become A popular research topic. Computer-aided lung cancer diagnosis technology is mainly the process of processing and analyzing the lung image data obtained by medical instrument imaging. The process is summarized into four steps: medical image data preprocessing, lung parenchymal segmentation, lung Nodule detection and segmentation, as well as lesion diagnosis. In order to solve the problem that the two-dimensional image model is not applicable to three-dimensional images, this paper proposes a three-dimensional convolutional neural network model suitable for lung cancer diagnosis. The model consists of two parts. The first part is a three-dimensional deep nodule detection network (FCN) model, which generates a heat map of the lung nodules. We can locate the locations of those malignant nodules through the heat map. According to the heat map generated in the first part, the second part selects those malignant nodules that are likely to be large, and then fuses the features of these selected nodules into one feature vector, showing the whole lung scan. Finally, we use this feature to classify and determine whether we have lung cancer.
机译：癌症是21世纪死亡率高的疾病之一，肺癌在所有癌症发病率和死亡中排名第一。近年来，随着大数据和人工智能的兴起，基于深度学习的肺癌辅助诊断逐渐成为一个流行的研究主题。计算机辅助肺癌诊断技术主要是通过医疗器械成像获得的肺图像数据的加工和分析过程。该过程总结为四个步骤：医学图像数据预处理，肺实质分割，肺结节检测和分割，以及病变诊断。为了解决二维图像模型不适用于三维图像的问题，本文提出了一种适用于肺癌诊断的三维卷积神经网络模型。该模型由两部分组成。第一部分是三维深对结节检测网络（FCN）模型，其产生肺结节的热图。我们可以通过热图找到恶性结节的位置。根据第一部分生成的热图，第二部分选择可能大的恶性结节，然后将这些所选结节的特征熔化成一个特征载体，显示整个肺部扫描。最后，我们使用此功能进行分类和确定我们是否有肺癌。

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  • 来源
  • 作者

    Yi Xiaochen; Sun Zongze; Yu Baolong; Yang Munan; Zhang Zhuo;

  • 作者单位

    Mudanjiang Med Coll Affiliated Hosp 2 Image Div 15 Dongxiaoyun St Mudanjiang City 157000 Heilongjiang Peoples R China;

    Mudanjiang Med Coll Affiliated Hosp 2 Image Div 15 Dongxiaoyun St Mudanjiang City 157000 Heilongjiang Peoples R China;

    Mudanjiang Med Coll Affiliated Hosp 2 Image Div 15 Dongxiaoyun St Mudanjiang City 157000 Heilongjiang Peoples R China;

    Mudanjiang Med Coll Affiliated Hosp 2 Image Div 15 Dongxiaoyun St Mudanjiang City 157000 Heilongjiang Peoples R China;

    Mudanjiang Med Coll Affiliated Hosp 2 Image Div 15 Dongxiaoyun St Mudanjiang City 157000 Heilongjiang Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 放射卫生;
  • 关键词

    CT Scan; Lung Cancer; Deep Learning; Artificial Intelligence;

    机译：CT扫描;肺癌;深入学习;人工智能;

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