Detecting Lung Nodules in Chest CT Images with Ensemble Relevance Vector Machine

Abstract:

Recent development of the lung nodule computer-aided diagnosis (CAD) in helical computed tomography (CT) images has shown great potential in the diagnosis and treatment of the lung cancer. One key technology of the CAD system is the classification of the nodules and non-nodules. In this paper, we try to solve the problem using the ensemble relevance vector machine (ERVM). The contribution of our work includes: 1) relevance vector machine (RVM) is used as the classifier of the CAD system. It has been proven that RVM is comparable to SVM in the generalization capability with a much sparser solution; 2) the ensemble skill is used to process the imbalanced candidates, to acquire a high detection rate with relative low false positives. The proposed method is evaluated using 20 helical CT scans, provided by Guangzhou military hospital. Compared with SVM, RVM is slightly better in accuracy. However, it is much sparser, showing great potential in predicting the huge volume CT scans. 84% of the 25 true nodules are identified by ERVM and only 9.8% of the non-nodules are misclassified. A 40% gain in sensitivity is acquired with the ensemble skill. The results show a fast and satisfactory classification rate.

• Periodical: Applied Mechanics and Materials (Volumes 195-196)
• Pages: 544-549
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.195-196.544
• Online since: August 2012
• Authors: Chao Dong, Lian Fang Tian, Jing Zhang, Bin Li
• Keywords: Computer Aided Diagnosis, Ensemble, Lung Nodules, Relevance Vector Machine

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