Lung Nodule Classification Using CT Images

Abstract

Determining lung tumor level and reducing patient mortality is a challenging task. So, the identification of benign or malignant lung nodules requires efficient and accurate methods of lung nodule diagnosis. For achieving this aim, in this paper, an adaptive radial basis neural network (RBNN) is proposed. Initially, the texture features are extracted and the extracted features are fed to the classifier to classify a nodule as benign or malignant nodule. In addition, the radial basis neural network is enhanced by using red deer optimization algorithm, which is used for optimal parameter selection. The effectiveness of the proposed approach is calculated by using different evaluation metrics. The effectiveness of the classification performance is compared with existing algorithms.