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首页> 外文期刊>International journal of imaging systems and technology >Morphological feature extraction and KNG-CNN classification of CT images for early lung cancer detection
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Morphological feature extraction and KNG-CNN classification of CT images for early lung cancer detection

机译：早期肺癌检测CT图像的形态特征提取和KNG-CNN分类

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摘要

Lung cancer is a dangerous disease causing death to individuals. Currently precise classification and differential diagnosis of lung cancer is essential with the stability and accuracy of cancer identification is challenging. Classification scheme was developed for lung cancer in CT images by Kernel based Non-Gaussian Convolutional Neural Network (KNG-CNN). KNG-CNN comprises of three convolutional, two fully connected and three pooling layers. Kernel based Non-Gaussian computation is used for the diagnosis of false positive or error encountered in the work. Initially Lung Image Database Consortium image collection (LIDC-IDRI) dataset is used for input images and a ROI based segmentation using efficient CLAHE technique is carried as preprocessing steps, enhancing images for better feature extraction. Morphological features are extracted after the segmentation process. Finally, KNG-CNN method is used for effectual classification of tumour 30mm. An accuracy of 87.3% was obtained using this technique. This method is effectual for classifying the lung cancer from the CT scanned image.
机译：肺癌是一种危险的疾病，导致个人死亡。目前精确分类和鉴别诊断肺癌是必不可少的，癌症鉴定的稳定性和准确性是挑战性的。基于核的非高斯卷积神经网络（KNG-CNN），在CT图像中为肺癌开发了分类方案。 KNG-CNN包括三个卷积，两个完全连接和三个合并层。基于内核的非高斯计算用于诊断工作中遇到的误报或错误。最初的肺图像数据库联盟图像集合（LIDC-IDRI）数据集用于输入图像，并且使用高效的CLAHE技术的基于ROI的分割作为预处理步骤，增强图像以进行更好的特征提取。在分割过程之后提取形态学特征。最后，KNG-CNN方法用于肿瘤和GT的有效分类。 30毫米。使用该技术获得了87.3％的准确性。该方法是有效的，用于将肺癌与CT扫描图像分类。

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