Kailasam S Piramu, Sathik M Mohamed
Research Scholar, Research and Development Centre, Bharathiar University,Coimbatore, India. Email:
Asian Pac J Cancer Prev. 2019 Feb 26;20(2):457-468. doi: 10.31557/APJCP.2019.20.2.457.
In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on early diagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network (DCNN) method is used for feature extraction and hybridize as combination of Convolutional Neural Network (CNN), Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradients (ExHOG) and Local Binary Pattern (LBP). A combination of shape, texture, scaling, rotation, translation features extracted using HOG, LBP and CNN. The Homogeneous descriptors used to extract the feature of lung images from Lung Image Database Consortium (LIDC) are given to classifiers Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree and Random Forest to classify nodules and non-nodules. Experimental results demonstrate the effectiveness of the proposed method in terms of accuracy which gives best result than the competing methods.
在本文中，一种改进的计算机辅助设计系统可以就CT（计算机断层扫描）图像上肺结节的早期诊断向放射科医生提供第二种意见。采用深度卷积神经网络（DCNN）方法进行特征提取，并将其与卷积神经网络（CNN）、方向梯度直方图（HOG）、扩展方向梯度直方图（ExHOG）和局部二值模式（LBP）相结合。使用HOG、LBP和CNN提取形状、纹理、缩放、旋转、平移特征的组合。用于从肺部图像数据库联盟（LIDC）中提取肺部图像特征的同质描述符被提供给支持向量机（SVM）、K近邻（KNN）、决策树和随机森林等分类器，以对结节和非结节进行分类。实验结果证明了该方法在准确性方面的有效性，其结果优于竞争方法。
