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Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM

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Abstract

Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC–IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC–IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.

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Acknowledgments

The authors acknowledge Coordination for the Improvement of Higher Education Personnel (CAPES), 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 financial support.

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Authors and Affiliations

  1. Applied Computing Group - NCA, Federal University of Maranhão - UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, 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, Rio de Janeiro, RJ, 20550-900, 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, Rio de Janeiro, RJ, 22453-900, 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. Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM. Med Biol Eng Comput 55, 1129–1146 (2017). https://doi.org/10.1007/s11517-016-1577-7

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  • DOI: https://doi.org/10.1007/s11517-016-1577-7

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