nCD and Clipped RBM Based Multimode DBN for Optimal Classification of Heterogeneous Images in Big Data
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.
The scientific community has shown a keen interest in the application of big data analytics in the healthcare industry. The management of healthcare records is extremely challenging not just because of the sheer volume of these records but also due to the multifaceted nature of the data sets and the high dimensions of these records. Recently, it has been demonstrated that deep learning models are extremely powerful generative models that are able to progressively separate features and provide great predictive execution. When it comes to medical image processing, traditional algorithms have been established for a particular modality and a specific condition. Because of the high memory and processing needs of each neural network, it is challenging to build a system that utilises a large number of neural networks and a wide range of specialised image-processing algorithms. This study proposed a C-RBM and nCD-based multimode DBN method. In the first stage, we use nCD (neutral Contrastive Divergence) to train unimodal CRBM pathways, and in the second stage, we build a multimode DBN architecture using only the shared representations of the two pathways. This method fundamentally comprises two-stage learning techniques. A computerised method for the classification of breast and brain cancer was represented by the multimode technique that was mentioned above. When it comes to accuracy, the recommended methodological setups perform better than the alternatives that are considered to be state-of-the-art.
Keywords
RBM (Restricted Boltzmann Machine, Deep Learning, Big Data, Deep Belief Network (DBN)), Computed Tomography (CT), Transfer 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: Franklin Vinod D;
Software: Neha Ahlawat and Franklin Vinod D;
Data Curation: Neha Ahlawat and Franklin Vinod D;
Writing- Original Draft Preparation: Franklin Vinod D;
Visualization: Neha Ahlawat and Franklin Vinod D;
Investigation: Franklin Vinod D;
Supervision: Neha Ahlawat and Franklin Vinod D;
Validation: Franklin Vinod D;
Writing- Reviewing and Editing: Neha Ahlawat and Franklin Vinod D;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
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, “nCD and Clipped RBM Based Multimode DBN for Optimal Classification of Heterogeneous Images in Big Data”, Journal of Machine and Computing, pp. 682-693, April 2025, doi: 10.53759/7669/jmc202505054.
