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. In this paper, 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. The Computer Aided Diagnosis
(CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT)
images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature
sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the
most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification
performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that
computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and
may contribute to more efficient diagnosis.
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