Depression Detection Using Atlas from fMRI Images
Document Type
Conference Proceeding
Publication Date
12-1-2020
Abstract
Major Depression Disorder (MDD) affects people's life and it is a common disorder worldwide. Finding useful diagnostic biomarkers would help clinicians to diagnosis MDD in its early stages. Having automated methods to find biomarkers for MDD is beneficial although it is challenging. In this paper, we investigate deep learning approaches versus functional connectivity-based approach for MDD classification using resting state fMRI data. To reduce data dimension, we have used Smith atlas. We compare several feature extraction methods. Experiments show promising results with connectivity-based approach. Moreover, applying T-test for finding discriminative features effectively improves the performance.
Publication Source (Journal or Book title)
Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
First Page
1348
Last Page
1353
Recommended Citation
Mousavian, M., Chen, J., & Greening, S. (2020). Depression Detection Using Atlas from fMRI Images. Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, 1348-1353. https://doi.org/10.1109/ICMLA51294.2020.00210
