Big Data Driven Multimodal Classification Using Clipped RBMs and Cross Modality Attention in MMDBN
Neha Ahlawat
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh, India.
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh, India.
Advancements in medical imaging and data acquisition have led to an exponential increase in high-dimensional, heterogeneous cancer data, necessitating scalable and intelligent diagnostic frameworks. In response, we propose a Modified Multimodal Deep Belief Network (MMDBN) architecture, built entirely upon Clipped Restricted Boltzmann Machines (CRBMs) with modified Contrastive divergence and augmented with a Cross-Modality Attention Fusion (CMAF) mechanism. This architecture is optimized for distributed big data environments, enabling real-time, high-throughput analysis of brain, breast, and bone cancer modalities. The Clipped RBM layers ensure bounded activation dynamics for robust unsupervised feature learning, mitigating instability and overfitting in large-scale training scenarios. CMAF adaptively weighs modality-specific representations per instance, improving generalization and interpretability, especially under incomplete or noisy modality conditions. Fine-tuning of the stacked network leverages supervised learning to optimize discriminative capacity across modalities. Empirical evaluation on benchmark medical datasets demonstrates the superiority of the proposed model, achieving 96.78% classification accuracy, with an AUC-ROC of 94.80, outperforming conventional DBN, CNN, and SVM-based baselines. This work highlights a significant advancement in deep multimodal learning for oncology, bridging the gap between data-intensive computation and clinically relevant cancer diagnosis.
Keywords
Big Data, Multimodel, Deep Belief Network, Machine Learning.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Neha Ahlawat and Franklin Vinod D;
Methodology: Neha Ahlawat;
Supervision: Franklin Vinod D;
Validation: Neha Ahlawat;
Writing- Reviewing and Editing: Neha Ahlawat and Franklin Vinod D;
All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Franklin Vinod D for this research completion and support.
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Franklin Vinod D
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh, India.
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Cite this article
Neha Ahlawat and Franklin Vinod D, “Big Data Driven Multimodal Classification Using Clipped RBMs and Cross Modality Attention in MMDBN”, Journal of Machine and Computing, vol.5, no.3, pp. 1396-1402, July 2025, doi: 10.53759/7669/jmc202505110.
