A Method of Pulmonary Nodules Detection with Support Vector Machines

Lung cancer is one of the deadly and most common diseases in the world. Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. We present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. We performed several experiments with different kernels and differently balanced training sets. The results obtained show that cost-sensitive SVMs trained with unbalanced data sets achieve promising results in terms of sensitivity and specificity. The studies have shown a high potential for implementation of this system in clinical practice as a computer aided diagnosis (CAD) tool.