A New Method for Pulmonary Nodule Detection using Decision Trees

Tartar A. , Kilic N. , Akan A.

35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Osaka, Japonya, 3 - 07 Temmuz 2013, ss.7355-7359 identifier identifier identifier

  • Cilt numarası:
  • Doi Numarası: 10.1109/embc.2013.6611257
  • Basıldığı Şehir: Osaka
  • Basıldığı Ülke: Japonya
  • Sayfa Sayıları: ss.7355-7359


A computer-aided detection (CAD) can help radiologists in diagnosing of lung diseases at an early level. In this study, a new CAD system for pulmonary nodule detection from CT imagery is presented by using morphological features and patient information properties. Decision trees are utilized for classification and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. Proposed CAD system with random forest classifier result in 90.5 % sensitivity and 87.6 % specificity of detection performance.