title: Classification of pulmonary nodules by using hybrid features.
abstract: Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).
authors: Department of Engineering Sciences, Istanbul University, 34320 Avcılar, Istanbul, Turkey. atartar@istanbul.edu.tr
authors: Ahmet
authors: A
authors: Tartar
authors: Niyazi
authors: N
authors: Kilic
authors: Aydin
authors: A
authors: Akan
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019295:Computational Biology; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D055613:Multiple Pulmonary Nodules / Q000145:classification* / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D003074:Solitary Pulmonary Nodule / Q000145:classification* / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data
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