The Implementation of Bayesian Optimization for Automatic Parameter Selection in Convolutional Neural Network for Lung Nodule Classification

Lung cancer's high mortality rate makes early detection crucial. Machine learning techniques, especially convolutional neural networks (CNN), play a very important role in lung nodule detection. Traditional machine learning approaches often require manual feature extraction, while CNNs, as a specialized type of neural network, automatically learn features directly from the data. However, tuning CNN hyperparameters, such as network structure and training parameters, is computationally intensive. Bayesian Optimization offers a solution by efficiently selecting parameter values. This study develops a CNN classification model with hyperparameter tuning using Bayesian Optimization, achieving a 97.2% accuracy. Comparatively, Particle Swarm Optimization and Genetic Algorithm methods each resulted in 96.4% accuracy. The research concludes that Bayesian Optimization is an effective approach for CNN hyperparameter tuning in lung nodule classification.