IMRT Optimization Method for Patients with Lung Cancer Using Firefly and Genetic Algorithms

This study presents a multi-objective model for scheduling Intensity Modulated Radiotherapy Treatment (IMRT) in patients with lung cancer, based on Genetic Algorithms (GA). Cancer classification can be beneficial to forecast the results of certain diseases or to find tumours' genetic behaviour. The suggested approach is used to minimise the fitness function defined as the mean squared error by optimizing the weight between layers and biases. The data set consists of 120 cases of CT indicators of lung cancer, 26 of which are chosen as the basis of diagnosis of lung cancer. In this study, a new approach is used to establish an accurate classification model by combining the recently developed heuristic algorithm Firefly Algorithm with the Genetic Algorithm in order to optimiseoptimize the values of weights and biases with the purpose of decreasing the mean squared error (mse), which is the study’s objective function. When compared to existing algorithms, the suggested GA-based Firefly Algorithm (FA) technique was shown to have the lowest mean squared error of 0.0014. The GA schedules, using real data acquired at the Cancer Center in collaboration, are effective. The suggested GA-based FA is discussed and assessment results are displayed. In terms of accuracy and mse, the proposed model, which is based on the GA, beats other algorithms, according to the evaluation data.