Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE.
Journal:
Computational and mathematical methods in medicine
Published Date:
Apr 6, 2015
Abstract
In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a novel SVM classifier combined with random undersampling (RU) and SMOTE for lung nodule recognition. The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data. Eight features including 2D and 3D features are extracted for training and classification. Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.
Authors
Keywords
Algorithms
Cluster Analysis
Diagnosis, Computer-Assisted
Humans
Imaging, Three-Dimensional
Lung Neoplasms
Models, Statistical
Pattern Recognition, Automated
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results
Solitary Pulmonary Nodule
Support Vector Machine
Tomography, X-Ray Computed