• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on machine vision >Early Detection of Lung Cancer from CT Images: Nodule Segmentation and Classification Using Deep Learning
【24h】

Early Detection of Lung Cancer from CT Images: Nodule Segmentation and Classification Using Deep Learning

机译：从CT图像中早期检测肺癌：使用深度学习进行结节分割和分类

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Lung cancer is one of the most abundant causes of the cancerous deaths worldwide. It has low survival rate mainly due to the late diagnosis. With the hardware advancements in computed tomography (CT) technology, it is now possible to capture the high resolution images of lung region. However, it needs to be augmented by efficient algorithms to detect the lung cancer in the earlier stages using the acquired CT images. To this end, we propose a two-step algorithm for early detection of lung cancer. Given the CT image, we first extract the patch from the center location of the nodule and segment the lung nodule region. We propose to use Otsu method followed by morphological operations for the segmentation. This step enables accurate segmentation due to the use of data-driven threshold. Unlike other methods, we perform the segmentation without using the complete contour information of the nodule. In the second step, a deep convolutional neural network (CNN) is used for the better classification (malignant or benign) of the nodule present in the segmented patch. Accurate segmentation of even a tiny nodule followed by better classification using deep CNN enables the early detection of lung cancer. Experiments have been conducted using 6306 CT images of LIDC-IDRI database. We achieved the test accuracy of 84.13%, with the sensitivity and specificity of 91.69% and 73.16%, respectively, clearly outperforming the state-of-the-art algorithms.
机译：肺癌是全世界癌症死亡的最丰富原因之一。其存活率低主要是由于诊断晚。随着计算机断层扫描（CT）技术的硬件进步，现在可以捕获肺区域的高分辨率图像。但是，它需要通过有效的算法加以增强，以便使用所获取的CT图像在早期阶段检测肺癌。为此，我们提出了一种用于肺癌早期检测的两步算法。给定CT图像，我们首先从结节的中心位置提取斑块并分割肺结节区域。我们建议使用Otsu方法，然后使用形态学运算进行分割。由于使用了数据驱动的阈值，因此此步骤可以实现准确的细分。与其他方法不同，我们无需使用结节的完整轮廓信息即可执行分割。第二步，使用深度卷积神经网络（CNN）对分段斑块中存在的结节进行更好的分类（恶性或良性）。即使是微小结节的准确分割，然后使用深层CNN进行更好的分类，也可以及早发现肺癌。使用LIDC-IDRI数据库的6306 CT图像进行了实验。我们达到了84.13％的测试准确度，灵敏度和特异性分别为91.69％和73.16％，明显优于最新算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号