The current clinical diagnosis of peripheral lung cancer is affected by many factors, which leads to certain uncertainty in the diagnosis results. In order to improve the clinical diagnosis of peripheral lung cancer, based on genetic algorithm, this study constructs a proprietary model for lung cancer detection and diagnosis, designs corresponding training methods and image processing methods in the model, and outputs clinically identifiable diagnostic images for clinical analysis. In order to study the role of genetic algorithm in clinical diagnosis, a comparative trial was designed to compare the clinical diagnosis results with the HRCT method. The experimental results show that the genetic algorithm based on cross-validation optimization has good clinical results in the diagnosis and analysis of peripheral lung cancer and can provide theoretical reference for subsequent related research.
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