Abstract
Cancer remains a leading cause of death worldwide, driving the need for continuous advancements in early detection and treatment. Deep learning, a subset of artificial intelligence, has become a transformative tool in medical image analysis, significantly improving cancer diagnosis. This study explores various modalities used in lung cancer diagnosis, including medical imaging (e.g., radiology, pathology), genomics, and clinical data, addressing the specific challenges of each domain. The proposed Multimodal Fusion Deep Neural Network (MFDNN) effectively integrates these diverse data sources to enhance diagnostic accuracy. Additionally, it emphasizes the integration of clinical data and electronic health records, demonstrating the value of multimodal approaches for improving reliability in lung cancer diagnosis. Ethical considerations related to AI in clinical settings, along with the need for validation and regulatory guidelines, are also discussed.