Lung nodule classification in CT images using Grey Wolf Optimization algorithm

Abstract

Medical image processing is of significant importance to accurately diagnose lung cancer and assists the physicians for better diagnosis. The early detection of pulmonary lung nodules is critical in the diagnosis of lung cancer and significantly increases the survival rate of affected people. Computed Tomography (CT) imaging technique of tumor detection is widely used to recognize the cancer regions. Feature selection and parameter optimization are the effective methods to improve the results of classifier. This paper proposes a novel Computer Aided Diagnosis (CAD) system based on Grey Wolf Optimizer (GWO) and Fuzzy Relevance Vector Machine (FRVM). GWO algorithm is applied as a feature selection method, that chooses best features subset from a huge set of features extracted from the lung CT images for improving the classification accuracy and then serial fusion is performed.The FRVM classifier is used for the classification of normal and abnormal CT scan lung images and the generalization ability of the classifier is evaluated using various performance metrics. The validation of the proposed classification scheme is empirically tested on Lung Image Database Consortium and Image Database Resource Initiative public database. The experimental results show that the performance of the proposed method outperforms several existing methods with improved accuracy.