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.

中文翻译：

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CT图像的形态特征提取和KNG-CNN分类用于早期肺癌检测

肺癌是一种导致个人死亡的危险疾病。目前肺癌的精确分类和鉴别诊断至关重要，癌症识别的稳定性和准确性具有挑战性。基于内核的非高斯卷积神经网络 (KNG-CNN) 为 CT 图像中的肺癌开发了分类方案。KNG-CNN 由三个卷积层、两个全连接层和三个池化层组成。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，肺图像数据库联盟图像收集 (LIDC-IDRI) 数据集用于输入图像，并使用高效的 CLAHE 技术进行基于 ROI 的分割作为预处理步骤，增强图像以更好地提取特征。在分割过程之后提取形态特征。最后，使用 KNG-CNN 方法对大于 30mm 的肿瘤进行有效分类。使用这种技术获得了 87.3% 的准确率。该方法对于从CT扫描图像中对肺癌进行分类是有效的。