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Abstract

Purpose

This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning.

Methods

Three features based on (i) Gabor filter, (ii) multi-resolution local binary pattern (LBP) texture features and (iii) signed distance fused with LBP which generates a combinational shape and texture feature are utilized to provide feature descriptors of malignant and benign nodules and non-nodule regions of interest. Support vector machines (SVMs) and k-nearest neighbor (kNN) classifiers in serial and two-tier cascade frameworks are optimized and analyzed for optimal classification results of nodules.

Results

A total of 1191 nodule and non-nodule samples from the Lung Image Data Consortium database is used for analysis. Classification using SVM and kNN classifiers is examined. The classification results from the two-tier cascade SVM using Gabor features showed overall better results for identifying non-nodules, malignant and benign nodules with average area under the receiver operating characteristics (AUC-ROC) curves of 0.99 and average f1-score of 0.975 over the two tiers.

Conclusion

In the results, higher overall AUCs and f1-scores were obtained for the non-nodules cases using any of the three features, showing the greatest distinguishability over nodules (benign/malignant). SVM and kNN classifiers were used for benign, malignant and non-nodule classification, where Gabor proved to be the most effective of the features for classification. The cascaded framework showed the greatest distinguishability between benign and malignant nodules.

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Corresponding author

Correspondence to Amal A. Farag.

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Conflict of interest

The authors declare they have no conflict of interests.

Ethical approval

All procedures performed in studies involving human participants were in accordance with ethical standards of the institutional and/or national research committee.

Funding

This research was conducted in collaboration between Kentucky Imaging Technologies and the University of Louisville, Computer Vision and Imaging Processing Laboratory.

Informed consent

The research conducted in this paper utilized the retrospective and publicly available LIDC database, and as such no formal consent was necessary.

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Farag, A.A., Ali, A., Elshazly, S. et al. Feature fusion for lung nodule classification. Int J CARS 12, 1809–1818 (2017). https://doi.org/10.1007/s11548-017-1626-1

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  • DOI: https://doi.org/10.1007/s11548-017-1626-1

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