Abstract:
:Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC-IDRI database, and cross-validation with k-fold, where [Formula: see text], was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Filho AOC,Silva AC,de Paiva AC,Nunes RA,Gattass Mdoi
10.1007/s11517-016-1582-xsubject
Has Abstractpub_date
2017-08-01 00:00:00pages
1199-1213issue
8eissn
0140-0118issn
1741-0444pii
10.1007/s11517-016-1582-xjournal_volume
55pub_type
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