Multi-Channel Deep Neural Network For Temporal Lobe Epilepsy Classification Using Multimodal Mri Data
Abstract
Multiple magnetic resonance imaging (MRI) modalities are currently used for the diagnosis and characterization of temporal lobe epilepsy (TLE). The objective of this study is to assess the performance of individual and combination of multimodal MRI datasets to provide an accurate classification of TLE by employing a multi-channel deep neural network. Several multi-channel deep neural network models were trained, validated, and tested using brain structure metrics from structural MRI, MRI-based region of interest correlation features, and personal demographic and cognitive data (PDC). The results show that PDC individually offered the most accurate classification of TLE followed by the combination of PDC with MRI-based brain structure metrics. These findings demonstrate the potential of deep learning approaches such as mDNN models to combine multiple datasets for TLE classification.
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Deep learning methods like mDNN models have potential in TLE classification when combined with multiple datasets.
“These findings affirm the potential of deep learning methods, like mDNN models, in TLE classification when combined with multiple datasets.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [174]
PDC alone provided the most accurate TLE classification, followed by the combination of PDC with MRI-based brain structural indices.
“Results indicated that PDC alone provided the most accurate TLE classification, followed by the combination of PDC with MRI-based brain structural indices.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [174]
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- Authors
- Torres-Velazquez, M., Hwang, G., Cook, C. J., Hermann, B., Prabhakaran, V., Meyerand, M. E., & McMillan, A. B.
- Affiliations
- University of Wisconsin–Madison (US)
- ORCID
- Venue
- 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)
- Publisher
- IEEE
- Published
- 2020-04
- Pages
- 1-4
- OpenAlex
- W3046420760
- Cited by
- 13
- References
- 17
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 2
- FWCI
- 2.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- temporal lobe epilepsy, epilepsy classification, multimodal MRI, multimodal fusion
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