Multi-Modal Deep Learning for Disease Diagnosis

Abstract

Accurate disease diagnosis often requires the integration of diverse data sources such as medical images, clinical records, laboratory reports, and patient history. This paper presents a Multi-Modal Deep Learning framework for Disease Diagnosis that leverages heterogeneous healthcare data to improve diagnostic performance. The proposed system combines multiple data modalities using deep learning architectures, including Convolutional Neural Networks for image analysis and Recurrent Neural Networks for sequential clinical data processing. Feature fusion techniques are employed to integrate information from different modalities, enabling comprehensive representation and improved decision-making. The framework is further enhanced through hybrid and ensemble learning approaches to increase robustness and generalization across varied datasets. Experimental results demonstrate that the proposed model outperforms single-modality approaches in terms of accuracy and reliability. Additionally, integration with prior research in machine learning and healthcare prediction strengthens the effectiveness of the system. The study highlights the potential of multi-modal deep learning in enabling precise, data-driven diagnosis and supporting advanced clinical decision support systems, particularly in complex and resource-constrained healthcare environments.