期刊
JOURNAL OF DIGITAL IMAGING
卷 30, 期 6, 页码 812-822出版社
SPRINGER
DOI: 10.1007/s10278-017-9973-6
关键词
Lung cancer; Phylogenetic diversity index; Genetic algorithm; Medical image
资金
- Coordination for the Improvement of Higher Education Personnel (CAPES)
- National Council for Scientific and Technological Development (CNPq)
- Foundation for the Protection of Research and Scientific
- Technological Development of the State of Maranhao (FAPEMA)
- Foundation for Research Support of the State of Piaui (FAPEPI)
Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for 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. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes. After that, we applied the genetic algorithm for selection of the best model. In the tests' stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules. The proposed work presents promising results at the classification into malignant and benign, achieving accuracy of 92.52%, sensitivity of 93.1% and specificity of 92.26%. The results demonstrated a good rate of correct detections using texture features. Since a precocious detection allows a faster therapeutic intervention, thus a more favorable prognostic to the patient, we propose herein a methodology that contributes to the area in this aspect.
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