Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection

Lung cancer has been the leading cause of death for many decades. With advent artificial intelligence, various machine learning models have proposed lung detection (LCD). Typically, challenges in building an accurate LCD model are small-scale datasets, poor generalizability to detect unseen data, and selection useful source domains prioritization multiple transfer learning. In this paper, a multiround modified generative adversarial network (MTL-MGAN) algorithm is LCD. The MTL transfers knowledge between prioritized target domain get rid exhaust search datasets among maximizing transferability with process, avoiding negative via customization loss functions aspects domain, instance, feature. regard MGAN, it not only generates additional training data but also creates intermediate bridge gap domains. 10 benchmark chosen performance evaluation analysis MTL-MGAN. significantly improved accuracy compared related works. To examine contributions individual components MTL-MGAN, ablation studies conducted confirm effectiveness algorithm, MTL, avoidance functions, MGAN. research implications feasibility enhance optimal solution provide generic approach using