Improving the Performance of Lung Cancer Detection at Earlier Stage and Prediction of Reoccurrence using the Neural Networks and Ant Lion Optimizer

Abstract

Lung cancer is considered to be the one among the most dreaded disease which will be the main reason for the death of individuals and having greater deterioration of death if it is not identified at primitive stage. Because of the fact that Lung cancer could be identified only after spreading to the parts of lungs to a greater extent and it is very tough to predict the presence of lung cancer at the earlier stage. Moreover, it involves greater error in the diagnosing the presence of Lung cancer by Radiologists and Expert Doctors. Therefor it is compulsory to design an intelligent and automated system for accurately predicting the cancer and stage at which the stage of cancer or enhancing the accuracy of prediction for detecting the cancer at earlier which will be much helpful in deciding the treatment type and depth of the treatment based on the extent of disease. Currently application of ANN strategies are the influential ways in supporting expert doctor for examining, complicated medical increase across a wider category of medical application. Back Propagation Network are ideal in recognizing lung cancer and there is no requirement involvement by expert doctors. Maximum number of applications of BPN in medical diagnosis will be utilized in the applications related to decision making of the presence or absence of disease; by which the performance will be reliant over the considered features and allocating the patient with minimum number of classes. Here this research paper establishes the idea of using BPN in the classification of the lung cancer and its stages and the predicting the possibility of recurrence. Along with the BPN, a nature inspired Meta Heuristics that is termed as Ant Lion Optimization Algorithm is used in optimizing the parameters and weights of Back Propagation Network. By using the Ant Lion Optimization Algorithm, the convergence mechanism is improved along with improving the accuracy of the proposed technique and it avoids the chance of getting caught within the clutches of local minima. By using this proposed method BPN network optimized with the help of antlion optimizer more accurate prediction of lung cancer is possible even at primitive stage and the predicting of chance of reoccurrence even after undergoing the appropriate treatment.