A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers

Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively.

[1]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[2]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[3]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[5]  Francis K. H. Quek,et al.  Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets , 2003, Pattern Recognit..

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[8]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[9]  Ilaria Gori,et al.  Lung nodule detection in low-dose and thin-slice computed tomography , 2008, Comput. Biol. Medicine.

[10]  Berkman Sahiner,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. , 2009, Medical physics.

[11]  Michael C. Lee,et al.  Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction , 2010, Artif. Intell. Medicine.

[12]  Abbas Z. Kouzani,et al.  Random forest based lung nodule classification aided by clustering , 2010, Comput. Medical Imaging Graph..

[13]  Donato Cascio,et al.  Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models , 2012, Comput. Biol. Medicine.

[14]  Aydin Akan,et al.  Classification of Pulmonary Nodules by Using Hybrid Features , 2013, Comput. Math. Methods Medicine.

[15]  Aydin Akan,et al.  Bagging support vector machine approaches for pulmonary nodule detection , 2013, 2013 International Conference on Control, Decision and Information Technologies (CoDIT).

[16]  A. Akan,et al.  A new method for pulmonary nodule detection using decision trees , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  Tae-Sun Choi,et al.  Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor , 2014, Comput. Methods Programs Biomed..