KEYWORDS: 3D modeling, Magnetic resonance imaging, Data modeling, Functional magnetic resonance imaging, Diagnostics, 3D image processing, Brain, Performance modeling, Neuroimaging, Diseases and disorders
Depression is one of the most common mental health disorders and has been a major focus of research, particularly through the lens of automated diagnostic methods. While many studies have explored magnetic resonance imaging techniques separately, the integration of multiple neuroimaging modalities has received less attention. To address this gap, we introduce a multimodal automatic classification method that leverages both resting-state functional magnetic resonance imaging and structural magnetic resonance imaging. Our approach employs a multi-stream 3D Convolutional Neural Network model to facilitate joint training on diverse features extracted from rs-fMRI and sMRI data. By classifying a combined group of 830 MDD patients and 771 normal controls from the REST-meta-MDD dataset, our model achieves an impressive accuracy of 69.38% using a feature combination of CSF, REHO, and fALFF. This result signifies a notable enhancement in classification performance, contributing valuable insights into the capabilities of multimodal imaging in MDD diagnosis.
The automatic diagnosis of depression using deep learning has recently shown significant progress, and 3DCNN has been applied to assessing depression levels from videos. There are two problems to be encountered applying 3DCNN, including insufficient data to train the model and an insufficient diversity of features extracted on a single scale. An approach is presented in this paper to address these issues. Firstly, we augmented the raw data by adding noise and applying rotations to increase the amount of the training set. Secondly, we constructed a 3DCNN to extract more diverse features from multiscales. We evaluated our proposed method on the AVEC 2014. Our approach achieved MAE=7.13 and RMSE=9.16, demonstrating its effectiveness in improving the accuracy of depression assessment compared to existing approaches.
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