Hybrid Deep Learning Architectures for Multimodal Data Fusion in Healthcare Diagnostics

Integrating multimodal data, such as medical imaging, electronic health records (EHRs), and genomic data, is critical for comprehensive healthcare diagnostics. However, these data sources' heterogeneity and high dimensionality present challenges in developing robust and accurate diagnostic models. This paper proposes a hybrid deep learning architecture that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models to achieve efficient multimodal data fusion for healthcare diagnostics. The proposed architecture leverages CNNs for extracting spatial features from image data, RNNs for capturing temporal dependencies in sequential data, and Transformers for cross-modality attention and fusion. A comprehensive evaluation of benchmark healthcare datasets, such as MIMIC-III, ChestX-ray14, and UK Biobank, demonstrates the model's superior diagnostic accuracy, interpretability, and generalization compared to existing methods. This study highlights the potential of hybrid deep learning architectures for improving diagnostic precision, enabling early disease detection, and facilitating personalized treatment strategies in real-world clinical settings. Future work will focus on enhancing model interpretability and reducing computational complexity for more practical deployment.