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3D shape analysis to reduce false positives for lung nodule detection systems

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Abstract

Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC–IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC–IDRI database, and cross-validation with k-fold, where \(k = 5\), was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer.

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Acknowledgments

The authors acknowledge the Coordination for the Improvement of Higher Education Personnel (CAPES), the National Council for Scientific and Technological Development (CNPq), and the Foundation for the Protection of Research and Scientific and Technological Development of the State of Maranhão (FAPEMA) for their financial support.

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Correspondence to Antonio Oseas de Carvalho Filho.

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Filho, A.O.d.C., Silva, A.C., de Paiva, A.C. et al. 3D shape analysis to reduce false positives for lung nodule detection systems. Med Biol Eng Comput 55, 1199–1213 (2017). https://doi.org/10.1007/s11517-016-1582-x

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  • DOI: https://doi.org/10.1007/s11517-016-1582-x

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