{"ID":2875751,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01164","arxiv_id":"2509.01164","title":"A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture","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.","short_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 var...","url_abs":"https://arxiv.org/abs/2509.01164","url_pdf":"https://arxiv.org/pdf/2509.01164v1","authors":"[\"Cheng Cheng\",\"Zeping Chen\",\"Xavier Wang\"]","published":"2025-09-01T06:37:20Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.IV\"]","methods":"[]","has_code":false}