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A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans

机译:在计算机断层扫描上自动对肺癌进行分类的计算机辅助管道

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摘要

Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3–10 mm), the proposed system can determine the nodule type with 94.68% accuracy.
机译:肺癌是最常见的癌症类型之一。对于患者的生存,采用最佳治疗方法及早发现肺癌至关重要。在这项研究中,由于良性和恶性结节的分类,我们提出了一种新型的计算机辅助计算机断层扫描(CT)扫描管道,以用于肺癌的早期诊断。拟议中的管道包括四个阶段。在预处理步骤中,将增强CT图像,并借助一种称为肺体积提取方法(LUVEM)的新方法从图像中提取肺体积。拟建管道的意义在于使用LUVEM提取肺区域。在结节检测阶段,根据基于循环霍夫变换(CHT-)的方法确定候选结节。然后,用自组织图(SOM)对肺结节进行分割。在特征计算阶段,强度,形状,纹理,能量和组合特征用于特征提取,主成分分析(PCA)用于特征减少步骤。在最后阶段,概率神经网络(PNN)对良性和恶性结节进行分类。根据对我们的数据集进行的实验,所提出的管道系统可以对良性和恶性结节进行分类,准确度为95.91%,敏感性为97.42%和特异性为94.24%。即使在小结节(3-10mm)的情况下,所提出的系统也可以以94.68%的精度确定结节类型。

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