...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Medical Imaging >3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography
【24h】

3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography

机译：3D-GLCM CNN：基于三维灰度共发生的基于麦克风分类的CNN模型，通过CT上影学

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a significant clinical impact on early detection and identifying optimal treatment of colorectal cancer. Convolution neural network (CNN) has shown great potential in recognizing different objects (e.g. human faces) from multiple slice (or color) images, a task similar to the polyp differentiation, given a large learning database. This study explores the potential of CNN learning from multiple slice (or feature) images to differentiate malignant from benign polyps from a relatively small database with pathological ground truth, including 32 malignant and 31 benign polyps represented by volumetric computed tomographic (CT) images. The feature image in this investigation is the gray-level co-occurrence matrix (GLCM). For each volumetric polyp, there are 13 GLCMs, computed from each of the 13 directions through the polyp volume. For comparison purpose, the CNN learning is also applied to the multi-slice CT images of the volumetric polyps. The comparison study is further extended to include Random Forest (RF) classification of the Haralick texture features (derived from the GLCMs). From the relatively small database, this study achieved scores of 0.91/0.93 (two-fold/leave-one-out evaluations) AUC (area under curve of the receiver operating characteristics) by using the CNN on the GLCMs, while the RF reached 0.84/0.86 AUC on the Haralick features and the CNN rendered 0.79/0.80 AUC on the multiple-slice CT images. The presented CNN learning from the GLCMs can relieve the challenge associated with relatively small database, improve the classification performance over the CNN on the raw CT images and the RF on the Haralick features, and have the potential to perform the clinical task of differentiating malignant from benign polyps with pathological ground truth.
机译：准确分类结肠直肠息肉，或将恶性从良性的息肉分类，对早期检测和鉴定结直肠癌的最佳治疗具有显着的临床影响。卷积神经网络（CNN）在识别来自多个切片（或颜色）图像的不同对象（例如人面），给定大型学习数据库时，识别来自多个切片（或颜色）图像的不同对象（例如人称）。本研究探讨了来自多个切片（或特征）图像的CNN学习的潜力，以将恶性从良性息肉与病态基础事实的相对较小的数据库区分，包括32个由体积计算断层（CT）图像表示的恶性和31个良性息肉。本研究中的特征图像是灰度级共发生矩阵（GLCM）。对于每个体积息肉，有13个GLCMS，通过息肉卷从13个方向中的每一个计算。为了比较目的，CNN学习也应用于体积息肉的多切片CT图像。比较研究进一步扩展到包括Haralick纹理特征的随机森林（RF）分类（来自GLCMS）。从相对较小的数据库中，通过使用GLCMS上的CNN，本研究通过使用CNN实现了0.91 / 0.93（双倍/ 0.93（两倍/左右评估）AUC（接收器操作特性的曲线区域），而RF达到0.84 /0.86在Haralick功能上的AUC，CNN在多切片CT图像上呈现0.79 / 0.80 AUC。从GLCMS学习的所呈现的CNN可以缓解与相对较小的数据库相关的挑战，提高原始CT图像上的CNN上的分类性能和Haralick特征上的RF，并且有可能执行分化恶性的临床任务良性息肉与病态的原始真相。

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