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