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. Therefore, this study proposes a methodology for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Mean phylogenetic distance (MPD) and taxonomic diversity index (Δ) were used as texture descriptors. Finally, the genetic algorithm in conjunction with the support vector machine were applied to select the best training model. The proposed 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 93.42%, specificity of 91.21%, accuracy of 91.81%, and area under the ROC curve of 0.94. The results demonstrate the promising performance of texture extraction techniques using mean phylogenetic distance and taxonomic diversity index combined with phylogenetic trees.
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Acknowledgements
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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This study is financially supported by CAPES and CNPq.
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de Sousa Costa, R.W., da Silva, G.L.F., de Carvalho Filho, A.O. et al. Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance. Med Biol Eng Comput 56, 2125–2136 (2018). https://doi.org/10.1007/s11517-018-1841-0
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DOI: https://doi.org/10.1007/s11517-018-1841-0