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Journal of Imaging Informatics in Medicine
Published in: Journal of Digital Imaging 4/2020

01-08-2020 | Pulmonary Nodule | Original Paper

Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules

Authors: Jumin Zhao, Chen Zhang, Dengao Li, Jing Niu

Published in: Journal of Imaging Informatics in Medicine | Issue 4/2020

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Abstract

Lung cancer has the highest mortality rate of all cancers, and early detection can improve survival rates. In the recent years, low-dose CT has been widely used to detect lung cancer. However, the diagnosis is limited by the subjective experience of doctors. Therefore, the main purpose of this study is to use convolutional neural network to realize the benign and malignant classification of pulmonary nodules in CT images. We collected 1004 cases of pulmonary nodules from LIDC-IDRI dataset, among which 554 cases were benign and 450 cases were malignant. According to the doctors’ annotates on the center coordinates of the nodules, two 3D CT image patches of pulmonary nodules with different scales were extracted. In this study, our work focuses on two aspects. Firstly, we constructed a multi-stream multi-task network (MSMT), which combined multi-scale feature with multi-attribute classification for the first time, and applied it to the classification of benign and malignant pulmonary nodules. Secondly, we proposed a new loss function to balance the relationship between different attributes. The final experimental results showed that our model was effective compared with the same type of study. The area under ROC curve, accuracy, sensitivity, and specificity were 0.979, 93.92%, 92.60%, and 96.25%, respectively.
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Metadata
Title
Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules
Authors
Jumin Zhao
Chen Zhang
Dengao Li
Jing Niu
Publication date
01-08-2020
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 4/2020
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-020-00333-1

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