Deep Fusion of Vision Transformers, Graph Neural Networks, and LayoutLM for Enhanced Multimodal Detection of Lung Cancer: A Novel Approach in Computational Oncology

An innovative AI framework has been designed to improve early lung cancer detection by combining Vision Transformers (ViT), Graph Neural Networks (GNNs), and LayoutLM. Utilizing both the IQ-OTH/NCCD and Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening datasets offers a varied and thorough data basis for training and validating models. ViT enables advanced image processing of CT scans by utilizing self-attention processes to capture intricate visual characteristics. Graph neural networks analyze intricate spatial patterns in images to enhance diagnostic precision. Simultaneously, LayoutLM analyzes unstructured text data from patient records, enhancing the depth of the study. This method aims to enhance the diagnostic skills of current models by thoroughly leveraging multimodal data for comprehensive cancer detection. By using two separate datasets, comprehensive testing and improvement are achieved, leading to enhanced performance across diverse patient demographics and conditions. The framework sets a new standard in precision oncology by incorporating advanced AI technologies, leading to significant advancements in early detection, patient care, and personalized treatment approaches. This contributes to transformative improvements in clinical diagnostic methods and patient results in lung cancer treatment.