Multi-Modal Deep Learning for Disease Diagnosis

Abstract

Accurate disease diagnosis often requires the integration of diverse healthcare 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 data to improve diagnostic accuracy and reliability. The proposed system integrates multiple deep learning architectures, including Convolutional Neural Networks for image processing and Recurrent Neural Networks for sequential clinical data analysis. Feature fusion techniques are employed to combine information from different modalities, enabling comprehensive representation learning and improved decision-making. The framework is further enhanced using hybrid and ensemble learning strategies to increase robustness and generalization across varied datasets. Experimental results demonstrate that the proposed approach outperforms single-modality models in terms of accuracy, precision, and recall. Additionally, integration with prior research in healthcare analytics strengthens system performance and adaptability. The study highlights the potential of multi-modal deep learning in enabling precise, data-driven diagnosis and supporting advanced clinical decision support systems in modern healthcare environments.