AN INTEGRATED DEEP LEARNING BASED ENHANCED GREY WOLF OPTIMIZATION FOR LUNG CANCER PREDICTION

Abstract

Lung cancer is an extremely harmful disease that represents the leading cause of death among both males and females within the nation. The survival spans for lung cancer patients within the 10%-20% range are limited to a duration of five years. Nevertheless, in the event that lung cancer is identified in its early stages and promptly treated, there is potential for a reduction in death rates. When lung cancer is identified at an early stage during the screening procedure, the clinical response to treatment may exhibit variability and provide very favourable outcomes. The implementation of a dependable and automated system might greatly facilitate the early identification of lung cancer, even in remote regions. This research presents a unique technique called Integrated Deep Learning-based Enhanced Grey Wolf Optimization for lung cancer prediction (IDL-EGWO). In order to address the issue of instability and convergence accuracy that occurs when using the Grey Wolf Optimizer (GWO) as a meta-heuristic algorithm with a robust capacity for optimum search, A weighted average GWO algorithm is suggested as a way to try to fix the problems with the GWO, such as the fact that it can get stuck in local optima and has a slow convergence rate in later stages. This technique incorporates an Artificial Neural Network (ANN) during the training phase. The research included a range of performance criteria, including precision, recall, f-measure, accuracy, execution time, and root mean squared error. According to the experiment, the IDL-EGWO algorithm demonstrated a higher accuracy rate of 97% compared to the previous methods. © Little Lion Scientific.