Multimodal Deep Belief Network with Layer-Wise Relevance Propagation: A Solution for Heterogeneous Image Challenges in Big Data

Abstract

Cancer is a complex and heterogeneous disease, with diverse molecular profiles and clinical outcomes. Accurate cancer classification is crucial for personalized treatment strategies and improved patient survival. The advent of high-throughput technologies has generated vast amounts of multi-dimensional data, including genomic, proteomic, and clinical information. Analyzing this "big data" requires sophisticated computational methods. This paper presents an improvised approach for Layer-wise Relevance Propagation (LRP) in Multimodal Deep Belief Networks (MDBNs) for cancer classification. By integrating Clipped Activation and Contrastive Divergence (CD), we enhance model interpretability and performance, addressing challenges like vanishing gradients and slow convergence. Our approach improves the efficiency of LRP while ensuring stable training and faster model convergence. Experiments on multimodal medical data, including brain, breast, and bone scans, demonstrate significant gains in classification accuracy and interpretability compared to traditional methods, offering a scalable solution for deep learning in healthcare.