Paper Info
Title
Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance.
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. Graphical Abstract Stages of the proposed methodology.
Year
DOI
Venue
2018
10.1007/s11517-018-1841-0
Med. Biol. Engineering and Computing
Keywords
Field
DocType
Medical image,Lung nodules diagnosis,Phylogenetic tree,Mean phylogenetic distance,Taxonomic diversity index
Diversity index,Lung cancer,Computer vision,Phylogenetic tree,Pattern recognition,Support vector machine,Image processing,Artificial intelligence,Computed tomography,Image database,Area under the roc curve,Mathematics
Journal
Volume
Issue
ISSN
56
11
0140-0118
Citations 
PageRank 
References 
0
0.34
15
Authors
6
Name
Order
Citations
PageRank
Robherson Wector de Sousa Costa100.34
Giovanni Lucca França da Silva2162.99
Antonio Oseas de Carvalho Filho3756.57
Aristofanes C. Silva431636.48
Anselmo C. Paiva537948.88
Marcelo Gattass638248.43