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Lung nodules diagnosis based on evolutionary convolutional neural network

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Abstract

Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. In this paper, we exploit a deep learning technique jointly with the genetic algorithm to classify lung nodules in whether malignant or benign, without computing the shape and texture features. The methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 94.66%, specificity of 95.14%, accuracy of 94.78% and area under the ROC curve of 0.949.

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Acknowledgements

The authors acknowledge CAPES and CNPq for their financial support. The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for their critical role in the creation of the free, publicly available LIDC-IDRI database used in this research.

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Authors and Affiliations

  1. Applied Computing Group (NCA), Federal University of Maranhão (UFMA), São Luís, MA, 65085-580, Brazil

    Giovanni L. F. da Silva, Otílio P. da Silva Neto, Aristófanes C. Silva & Anselmo C. de Paiva

  2. Pontifical Catholic University of Rio de Janeiro - PUC - Rio, Rio de Janeiro, RJ, 22453-900, Brazil

    Marcelo Gattass

Authors
  1. Giovanni L. F. da Silva
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  2. Otílio P. da Silva Neto
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  3. Aristófanes C. Silva
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  4. Anselmo C. de Paiva
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  5. Marcelo Gattass
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Correspondence to Giovanni L. F. da Silva.

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da Silva, G.L.F., da Silva Neto, O.P., Silva, A.C. et al. Lung nodules diagnosis based on evolutionary convolutional neural network. Multimed Tools Appl 76, 19039–19055 (2017). https://doi.org/10.1007/s11042-017-4480-9

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  • DOI: https://doi.org/10.1007/s11042-017-4480-9

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