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- W4385300817 abstract "The fMRI functional imaging technology has become an important means to detect brain diseases. It mainly diagnoses whether there are lesions in the patient’s brain region by monitoring the changes of the blood oxygen level (BOLD) in the brain region. Existing brain disease classification techniques are mainly achieved through network construction, feature extraction and classification model construction. But in the existing methods, almost no one uses the time attribute. In this paper, we propose a brain disease diagnosis framework with temporal attribute and feature fusion, use temporal and BOLD signal’s changes in different time periods to construct a brain connectivity network. We extract frequent sequences and sequences’ connection rules in the subjects’ brain connection network, and finally use pseudo-binary matrix to fuse the two features and finish classification model construction. Experiment turns out that we achieve better accuracy than other schemes." @default.
- W4385300817 created "2023-07-28" @default.
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- W4385300817 date "2022-12-01" @default.
- W4385300817 modified "2023-10-16" @default.
- W4385300817 title "A Brain Disease Classification Framework with Temporal Attribute and Feature Fusion" @default.
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- W4385300817 doi "https://doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00194" @default.
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