Title :
Performance of ensemble learning classifiers on malignant-benign classification of pulmonary nodules
Author :
Tartar, A. ; Akan, A.
Author_Institution :
Muhendislik Bilimleri Bolumu, Istanbul Univ., İstanbul, Turkey
Abstract :
Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this study, a novel Computer-aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. Proposed CAD system, providing an important support to radiologists at the diagnosis process of the disease, achieves high classification performance using ensemble learning classifiers.
Keywords :
diseases; learning (artificial intelligence); medical diagnostic computing; pattern classification; CAD system; computer-aided detection systems; computer-aided diagnosis system; disease diagnosis process; ensemble learning classifiers; high classification performance; malignant-benign classification; pulmonary nodule detection; Bagging; Biomedical imaging; Cancer; Computer aided diagnosis; Conferences; Lungs; Signal processing; computer-aided diagnosis system; ensemble learning classifiers; malignant-benign classification; pulmonary nodules;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2014 22nd
Conference_Location :
Trabzon
DOI :
10.1109/SIU.2014.6830331