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The classification of benign and malignant lung nodules has great significance for the early detection of lung cancer, since early diagnosis of nodules can greatly increase patient survival. In this paper, we propose a novel classification method for lung nodules based on hybrid features from computed tomography (CT) images. The method fused 3D deep dual path network (DPN) features, local binary pattern (LBP)-based texture features and histogram of oriented gradients (HOG)-based shape features to characterize lung nodules. DPN is a convolutional neural network which integrates the advantages of aggregated residual transformations (ResNeXt) for feature reuse and a densely convolutional network (DenseNet) for exploring new features. LBP is a prominent feature descriptor for texture classification, when combining with the HOG descriptor, it can improve the classification performance considerably. To differentiate malignant nodules from benign ones, a gradient boosting machine (GBM) algorithm is employed. We evaluated the proposed method on the publicly available LUng Nodule Analysis 2016 (LUNA16) dataset with 1004 nodules, achieving an area under the receiver operating characteristic curve (AUC) of 0.9687 and accuracy of 93.78%. The promising results demonstrate that our method has strong robustness on the classification of nodule patterns by virtue of the joint use of texture features, shape features and 3D deep DPN features. The method has the potential to help radiologists to interpret diagnostic data and make decisions in clinical practice.

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In this paper, an automatic, efficient and robust approach, performed on CT images, is proposed to determine the implanted seed location as well as orientation in a 3D workspace. Through the experiments with phantom and patient data, this approach also successfully exhibits good performance.