Grasshopper Optimization Algorithm-Generative Adversarial Network for Lung Cancer Detection and Classification

Lung cancer is one of the most dangerous deadly diseases for individuals worldwide. Thus, survival rate low due to difficulty in detecting lung at advanced stages like symptoms; thus, prominence early diagnosis important. The detection and treatment having great importance diagnosis. existing Convolution Neural Network (CNN) based deep learning methods showed tuning was problem choosing a set hyperparameters algorithm included outliers that affect classification result. Therefore, present research work aims utilize Grasshopper Optimization Algorithm (GOA) effectively solve global unconstrained constrained optimization issues. Additionally, performing training using Generative Adversarial (GAN) model controlled behavior classifier during significant impact. results proposed method gives better terms accuracy 98.89% when compared models such as KNG-CNN 87.3%, mask region-based CNN 97.68%, Transferable Texture 96.69%, Fuzzy Particle Swarm (FPSO) 95.62% E-CNN 97%.