A hybrid hierarchical multimodal learning with autoencoders, vision transformers and deep belief networks for liver cancer detection

T. Haripriya; K. Dharmarajan; Subrata Chowdhury; Amrutanshu Panigrahi; Abhilash Pati; Bibhuprasad Sahu

doi:10.1007/s44163-026-02027-z

August 16, 2026Discover Artificial IntelligenceOpen Access

A hybrid hierarchical multimodal learning with autoencoders, vision transformers and deep belief networks for liver cancer detection

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Key Points

To develop and evaluate a hybrid hierarchical multimodal deep learning framework integrating MRI and CT scans for early detection and classification of liver cancer.
Integrated a self-regularized autoencoder (AE), Vision Transformer (ViT), Deep Belief Network (DBN), and Deep Q-Network (DQN) for noise suppression, spatial dependency extraction, and decision optimization.
Trained and evaluated the architecture on a dataset consisting of 612 institutional patients and an external validation cohort of 512 public patients.
Achieved a classification accuracy of 98.76% (95% CI 98.12–99.34).
Demonstrated robust discriminative performance with an area under the ROC curve (AUC) of 0.992.

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Implication

Validation study demonstrates high diagnostic accuracy using multimodal deep learning for liver cancer detection in imaging cohorts, suggesting improved clinical decision support.

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Cite This Study

Haripriya et al. (2026) studied this question.

synapsesocial.com/papers/6a85632803308d306e2d62a6 https://doi.org/10.1007/s44163-026-02027-z

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A hybrid hierarchical multimodal learning with autoencoders, vision transformers and deep belief networks for liver cancer detection

Key Points

No abstract yet

Implication

Discussion

Cite This Study

A hybrid hierarchical multimodal learning with autoencoders, vision transformers and deep belief networks for liver cancer detection

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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