HRDEL: High ranking deep ensemble learning-based lung cancer diagnosis model

Abstract

Among all the diseases in human beings, lung cancer is known as the most hazardous disease that often leads to death rather than other cancer ailments. Lung cancer is asymptomatic, and so, it is unable to detect at the early stage. But, the rapid identification of lung cancer helps for sustaining the survival rate of people. Hence, many researchers develop various techniques for detecting lung cancer by undergoing different studies. Recently, computer technology has been used for solving these diagnosis problems. These developed systems involve diverse deep and machine learning approaches along with certain image-processing techniques for forecasting the severity level of lung cancer. Hence, this methodology plans to develop a novel intelligent method for diagnosing lung cancer. Initially, data is gathered by downloading two benchmark datasets, which include attribute information from various patients' health records. Furthermore, two standard techniques, “Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE)” have been used to extract features. Further, the deep features are retrieved from “the pooling layer of Convolutional Neural Network (CNN)”. Further to choose the significant features, the feature selection is taken place by the Best Fitness-based Squirrel Search Algorithm (BF-SSA), which is known as optimal feature selection. This hybrid optimization concept is considered to be superior in various domains to explore the search space efficiently and makes better performance in exploiting the feature selection. In the final phase called prediction, High Ranking Deep Ensemble Learning (HR-DEL) takes place concerning five forms of detection models. Finally, the high ranking of all the classifiers yields the final predicted output. The developed HR-DEL makes accurate prediction up to 8.79% better than the conventional methods and provides high robustness by reducing the dispersion or spread of the classification and model efficiency. The classification is performed, and the results are evaluated with the performance comparison of various algorithms.