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  • Received: Jul. 13, 2020

    Accepted: Sep. 15, 2020

    Posted: Dec. 1, 2020

    Published Online: Nov. 23, 2020

    The Author Email: Nie Shengdong (nsd4647@163.com)

    DOI: 10.3788/AOS202040.2410002

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    Dachuan Gao, Shengdong Nie. Method for Identifying Benign and Malignant Pulmonary Nodules Combing Deep Convolutional Neural Network and Hand-Crafted Features[J]. Acta Optica Sinica, 2020, 40(24): 2410002

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Acta Optica Sinica, Vol. 40, Issue 24, 2410002 (2020)

Method for Identifying Benign and Malignant Pulmonary Nodules Combing Deep Convolutional Neural Network and Hand-Crafted Features

Gao Dachuan and Nie Shengdong*

Author Affiliations

  • School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200082, China

Abstract

Here, we present a method for identifying benign and malignant pulmonary nodules that combines convolutional neural network(CNN)learning features and conventional hand-crafted features. First, the pulmonary nodules area is segmented from computed tomography (CT) images, and traditional machine learning methods are used to extract the image features of the nodule area. Then, the CNN features of network learning are extracted, using the intercepted pulmonary nodules to train the 3D-Inception-ResNet model, and the 2 kinds of features are combined, the random forest (RF) model is used for feature selection. Finally, support vector machine (SVM) and RF classifier are used to identify benign and malignant pulmonary nodules. The 1036 pulmonary nodules in the LIDC-IDRI database are used for experimental verification. Classification accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) of the proposed method can reach 94.98%, 90.02%, 97.03%, and 97.43%, respectively. The proposed method can accurately distinguish benign and malignant lung nodules, more effectively than most existing mainstream methods, as shown by the experimental results.

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