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Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM

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Abstract

The present work aims to develop a methodology for classifying lung nodules using the LIDC-IDRI image database. The proposed methodology is based on image-processing and pattern-recognition techniques. To describe the texture of nodule and non-nodule candidates, we use the Taxonomic Diversity and Taxonomic Distinctness Indexes from ecology. The calculation of these indexes is based on phylogenetic trees, which, in this work, are applied to the candidate characterization. Finally, we apply a Support Vector Machine (SVM) as a classifier. In the testing stage, we used 833 exams from the LIDC-IDRI image database. To apply the methodology, we divided the complete database into two groups for training and testing. We used training and testing partitions of 20/80 %, 40/60 %, 60/40 %, and 80/20 %. The division was repeated five times at random. The presented methodology shows promising results for classifying nodules and non-nodules, presenting a mean accuracy of 98.11 %. Lung cancer presents the highest mortality rate and has one of the lowest survival rates after diagnosis. Therefore, the earlier the diagnosis, the higher the chances of a cure for the patient. In addition, the more information available to the specialist, the more precise the diagnosis will be. The methodology proposed here contributes to this.

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Acknowledgments

The authors acknowledge 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 Maranho (FAPEMA) for financial support

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Authors and Affiliations

  1. Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil

    Antonio Oseas de Carvalho Filho, Aristófanes Corrêa Silva & Anselmo Cardoso de Paiva

  2. State University of Rio de Janeiro, Sao Francisco de Xavier, 524, Maracana, 20550-900, Rio de Janeiro, RJ, Brazil

    Rodolfo Acatauassú Nunes

  3. Department of Computer Science, Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. Marquês de São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil

    Marcelo Gattass

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  1. Antonio Oseas de Carvalho Filho
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Correspondence to Antonio Oseas de Carvalho Filho.

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de Carvalho Filho, A.O., Silva, A.C., de Paiva, A.C. et al. Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM. J Sign Process Syst 87, 179–196 (2017). https://doi.org/10.1007/s11265-016-1134-5

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  • DOI: https://doi.org/10.1007/s11265-016-1134-5

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