• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Pan American Health Care Exchanges Conference >Comparison of artificial neural networks using texture parameters in the recognition of lesions in mammograms digitized
【24h】

Comparison of artificial neural networks using texture parameters in the recognition of lesions in mammograms digitized

机译：在乳房X线照片中识别病变中的纹理参数的比较数字化的识别

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

摘要

This work proposes to use Radial Basis Function — RBF artificial neural network and Multi-Layer Perceptron MLP with the algorithm cross-validation leave-one-out, to reduce the false-positives of suspicious regions automatically detected by a difference-of-Gaussian filter in mammography. This method was applied to 175 mammograms (one real lesion/image), from the Digital Database for Screening Mammography. Was located and segmented 75.4% of lesions, with 3.55 false-positives/image. In this study, five texture parameters of real lesions and false-positive regions were extracted from a gray-level co-occurrence matrix. These parameters were input of the MLP network, trained with different backpropagation settings, and also input of the RBF network. False-positives were reduced to 1.38 per image, with 0.67 false-negatives per image. Future tests include a greater number of images to enhance the network generalization capacity.
机译：这项工作建议使用径向基函数 - RBF人工神经网络和多层的Perceptron MLP与算法交叉验证休假，以减少由高斯滤波器自动检测的可疑区域的假阳性 在乳房X线照相术中。 从数字数据库中施加到175个乳房X线照片（一个真实病变/图像），用于筛选乳房X光检查。 定位并分段为75.4％的病变，3.55个假阳性/图像。 在该研究中，从灰度共发生矩阵中提取了真实病变和假阳性区域的五个纹理参数。 这些参数被输入到MLP网络，具有不同的BackProjagation设置，也输入了RBF网络。 假阳性每张图像减少到1.38，每张图像具有0.67个假阴性。 未来的测试包括更多图像以增强网络泛化容量。

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