Optimizing Disease Detection: A Multi-Modal Deep Learning Framework for Medical Imaging and Clinical Data Integration

Abstract

This study introduces a novel multi-modal deep learning framework that integrates medical imaging data with clinical records for enhanced disease detection. We propose a hybrid architecture combining convolutional neural networks (CNNs) for image analysis and transformer networks for processing clinical data. The framework was evaluated on a dataset of 10,000 patients over 12 months, focusing on detecting early signs of lung cancer and coronary artery disease. Results show our integrated approach achieves significantly higher accuracy compared to single-modality models, with an F1 score of 0.89 (95% CI: 0.87-0.91, p < 0.001). We also introduce a novel interpretability metric for multi-modal models and demonstrate a 30% improvement in model explainability. These findings suggest our approach can enhance diagnostic accuracy while maintaining interpretability in clinical settings.