An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

Abstract

Lung cancer is one of the most serious and prevalent cancers in the globe. Early detection of lung cancer can increase a patient's chances of life. Computed Tomography (CT) scan images are difficult for clinicians to utilize in order to determine the stages of cancer. Computer-aided systems can assist researchers in more precisely predicting the stages of lung cancer in recent times. This study demonstrates the use of technology that is made possible by machine learning and image processing to accurately classify and predict the lung cancer from CT images. The existing tumor detection frameworks have the major difficulties in terms of high complexity, overfitting and error prediction. Therefore, the proposed work aims to formulate a simple and accurate automated system for the prediction and classification of lung cancer from CT images. Before classifying the input lung scan image, an adaptive median filtering approach is used to improve its contrast and quality. From the segmented lung parts, the histogram and texture features are derived. The most relevant characteristics are chosen using the Lion-Butterfly Optimization (LBO) method for training and testing operations. Eventually, the input CT picture is correctly predicted as either healthy or disease-affected using the Stacking Ensemble Learning Classification (SELC) algorithm. In this study, a thorough performance evaluation is conducted utilizing several measures in order to analyze the outcomes