Explainable Multimodal Deep Learning Framework For Early Disease Diagnosis

Early disease diagnosis remains a critical challenge in modern healthcare due to the fragmented nature of medical data, which often includes heterogeneous modalities such as medical imaging, clinical text, and physiological signals. Traditional deep learning models, while achieving high predictive performance, suffer from limited interpretability, thereby restricting their adoption in high-stakes clinical environments. This paper proposes an Explainable Multimodal Deep Learning Framework (EMDLF) designed to integrate and analyze diverse medical data sources while ensuring transparency in decision-making. The framework combines convolutional neural networks for image feature extraction, transformer-based architectures for textual understanding, and attention-driven fusion mechanisms for multimodal integration. To enhance interpretability, the model incorporates explainability techniques such as SHAP (Shapley Additive Explanations), Grad-CAM, and attention visualization, enabling clinicians to understand feature contributions and decision pathways. Experimental evaluation on benchmark healthcare datasets demonstrates improved diagnostic accuracy compared to unimodal baselines, along with meaningful explanation maps that align with clinical insights. The proposed approach not only improves predictive performance but also builds trust by offering interpretable outputs, making it suitable for real-world clinical deployment. This work contributes toward bridging the gap between high-performance AI systems and explainable, trustworthy healthcare solutions.