Optimization of Neural Networks for the Diagnosis and Prediction of Lung Cancer Stages

Abstract

Purpose: This study aims to optimize neural networks for diagnosing and predicting lung cancer stages, leveraging advanced recurrent neural network architectures such as Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks.

Materials and Methods: The research utilizes a comprehensive dataset of clinical data and high-resolution imaging to develop novel Deep Neural Networks (DNNs). These networks are optimized using sophisticated algorithms like Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (Adam). Transfer Learning (TL) techniques and big data analytics are integrated to enhance model precision and reliability. Additionally, early stopping is employed to prevent overfitting, k-fold cross-validation (k=5) is used to ensure robust model evaluation, and L2 regularization is applied to improve generalization by penalizing large weights.

Results: The optimized GRU and LSTM networks demonstrate superior performance in detecting lung cancer stages, significantly improving diagnostic accuracy and prognostic predictions. The models effectively handle long-term dependencies and mitigate the vanishing gradient problem, common in simple RNNs. Using k-fold cross-validation (k=5), the optimized GRU network with SGDM achieved the following metrics in Fold 3: Test Loss of 0.0531, Test Accuracy of 0.97, Sensitivity (Recall) of 0.97, Precision of 0.98, and an F1 Score of 0.975.

Conclusion: The study's findings underscore the potential of integrating advanced neural network models into clinical practice, revolutionizing AI-driven medical diagnostics and elevating the standard of patient care.