Enhanced Lung Cancer Detection Using Graph Convolutional Networks (GCN) Approach

Early detection of lung cancer is a critical factor in improving the survival rates of patients. In the meantime, current methods of diagnosis often do not cover the complete process as they bypass the disease’s initial stages. According to the survey, it suggests a unique way to lung cancer prediction involvement of Graph Convolutional Networks (GCNs) in the acquisition of diagnostic data to raise the accuracy and exactness of diagnostic models. This approach converts patient data from a tabular format to a graph structure, with each node representing a patient and edges representing similarities between two separate clinical features. The proposed GCN model is based on the idea of a multi-layer architecture, enabling it to effectively access both local and global patterns in the data, which in turn ensures the elapse of the performance of prediction. The model was evaluated with thoroughness and it showed the potential to replace the traditional machine learning algorithms in identifying the lung cancer cases correctly at a higher precision. Our methodology unites the advantages of graph convolution networks, so that it can reach our aim of greater accuracies and precision, which gives a good recognition for our approach of early lung cancer detection. The outcomes of the experiments confirm the value of graph-based deep learning models in medical diagnostics especially when the case is about complex tasks like lung cancer prediction. This study reflects the ongoing efforts on reliable and accurate diagnostic tools with the use of AI, thus facilitating the early detection of lung cancer and increasing patient’s survival.