A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture
This work addresses early diagnosis for liver cancer patients, but it appears incremental as it combines existing methods into a hybrid architecture.
The paper tackled early liver cancer diagnosis by proposing a multimodal deep learning framework integrating BiLSTM, attention, and VMD, which achieved superior performance over traditional and baseline models on real-world datasets.
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.