Smart Insights: Multimodal Deep Learning Techniques for Accurate Disease Identification

This paper presents a new multimodal deep learning framework for the identification of diseases through the fusion of medical imaging, EHR, and genomic data. This system runs, handles, and analyses heterogeneous medical data using a new fusion architecture combining convolutional neural networks, recurrent neural networks, and transformer models. Experimental results report better diagnostic accuracy for several diseases such as inflammatory bowel disease, type 2 diabetes, and liver cirrhosis. The system achieves a mean improvement in accuracy of 15% versus single-modality counterparts and performs well in the early detection of diseases. The system is scalable and adaptable for extended use at clinical setups.