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arXiv · 2509.01164

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture

Cheng Cheng·Zeping Chen·Xavier Wang
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Abstract

This paper proposes a novel multimodal deep learning framework integrating bidirectional LSTM, multi-head attention mechanism, and variational mode decomposition (BiLSTM-AM-VMD) for early liver cancer diagnosis. Using heterogeneous data that include clinical characteristics, biochemical markers, and imaging-derived variables, our approach improves both prediction accuracy and interpretability. Experimental results on real-world datasets demonstrate superior performance over traditional machine learning and baseline deep learning models.

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Cheng Cheng, Zeping Chen, Xavier Wang. 2025-09-01. A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture. https://arxiv.org/abs/2509.01164

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