Optimal deep belief network with opposition‐based hybrid grasshopper and honeybee optimization algorithm for lung cancer classification: A DBNGHHB approach

Abstract

AbstractIn this manuscript, a new approach using deep belief network (DBN) along opposition‐based hybrid grasshopper and honey bee optimization algorithm for lung cancer classification is proposed. Chest computed tomography (CT) is commonly used to diagnosis the lung tumors. Initially, the image quality is improved by preprocessing techniques, and then the features like texture, color and shape are extracted. Several functions have been originating from the second order first gray level statistics like modified angles, high‐order algebraic time invariant, Gaussian defining properties and new spectral power metrics. Recently, a local binary pattern geometric characteristic descriptor has been demonstrated in the extraction and classification of pulmonary nodules. Furthermore, some functionality is stripped away for comparison purposes. Dimension reduction is significant for the implementation of algorithms in machine learning. The other special approaches can be implemented to determine the functionalities due to vast set of features. To remove the unused and obsolete features between the feature selection approaches, the multivariate approach like local tangent space alignment is used. Finally, DBN is used to categorize pulmonary CT images as malignant or benign, and is calibrated to the detection of lung cancer classification by the opposition‐based hybrid grasshopper and honey bee optimization algorithm. The Lung Image Database Consortium including Image Resources Initiative database are analyzed the network services, and the experimental results shows that the DBN network represents 97.52% accuracy, 96% sensitivity and 94.58% specificity.