Integrating Deep Learning and Graph Neural Networks for Multimodal Lung Tumor Analysis: A Novel Approach for Improved Classification and Predict

- Lung cancer detection is of paramount importance for improving patient outcomes. This research focuses on the integration of deep learning and graph neural networks (GNNs) for multimodal lung tumour analysis. By leveraging the power of deep learning and GNN s, this study aims to address the limitations of existing approaches and improve the accuracy and interpretability of lung cancer detection. The proposed research first reviews the current state of lung cancer detection and highlights the need for advanced techniques. By integrating high-dimensional image features extracted from CT scans using a convolutional neural network (CNN) with the GNN framework, the model can effectively capture and analyze the relationships between CT images, clinical data, and molecular data. The research includes preprocessing steps, such as normalization and resizing, to ensure compatibility across the multimodal data. Through the construction of a graph structure, the spatial and functional connections between the multimodal data points are established. The GNN component is then designed and trained to process the graph structure and extract meaningful information from the data. The results of this research, as presented in the table, demonstrate the performance of the proposed GNN model compared to other neural network models. The GNN model achieved an accuracy of 85%, outperforming the CNN (80%) and MLP (75%) models. The precision, recall, and F1-score of the GNN model also surpassed those of the other models, indicating its superior predictive capabilities.