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

• URL: http://arxiv.org/abs/2509.01164v1
• Date: Mon, 01 Sep 2025 06:37:20 GMT
• Title: A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture
• Authors: Cheng Cheng, Zeping Chen, Xavier Wang,
• Abstract summary: 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.<n> Experimental results on real-world datasets demonstrate superior performance over traditional machine learning and baseline deep learning models.
• Score: 7.6708641505004005
• License: http://creativecommons.org/licenses/by/4.0/
• 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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