Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Rs-fMRI Data Preprocessing
2.3. Extraction of Time-Series
2.4. Separated Channel Convolutional Neural Network with an Attention Network (SC-CNN-Attention)
2.4.1. SC-CNN Network
2.4.2. Attention-Based Network
2.4.3. Classification Network
2.5. Model Optimization
2.6. Leave-One-Site-Out
3. Results
3.1. Classification Results Based on Multi-Site Data
3.2. Overall Classification Results and Comparison
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Site | ADHD | HC | Volumes | ||||
---|---|---|---|---|---|---|---|
Age | Count | Total | Age | Count | Total | ||
KKI | 8–13 | 10/15(F/M) | 35 | 8–13 | 28/41(F/M) | 69 | 152/119 |
NI | 11–21 | 5/31(F/M) | 36 | 12–26 | 25/12(F/M) | 37 | 257 |
NYU | 7–18 | 34/117(F/M) | 151 | 7–18 | 55/56(F/M) | 111 | 176/172 |
OHSU | 7–12 | 13/30(F/M) | 43 | 7–12 | 40/30(F/M) | 70 | 78/50/73 |
Peking | 8–17 | 10/92(F/M) | 102 | 8–15 | 59/84(F/M) | 143 | 236/231 |
Total | - | - | 422 | - | - | 597 | - |
NYU | Peking | OHSU | KKI | NI | Overall Accuracy | |
---|---|---|---|---|---|---|
Previous methods | ||||||
ADHD-200 competition [39] | 35.2% | 51.1% | 65.4% | 61.9% | 57.0% | 54.1% |
FCNet [24] | 58.5% | 62.7% | - | - | 60.0% | 60.4% |
3D-CNN [26] | - | 62.9% | - | 72.8% | - | 67.8% |
DeepFMRI [25] | 73.1% | 62.7% | - | - | 67.9% | 67.9% |
Our models | ||||||
SC-CNN-Dense | 55.4% | 60.3% | 59.8% | 69.2% | 63.0% | 61.3% |
SC-CNN | 52.4% | 60.2% | 61.6% | 68.1% | 64.4% | 61.5% |
SC-CNN-LSTM | 56.3% | 61.5% | 61.7% | 75.3% | 63.0% | 63.6% |
SC-CNN-Attention | 60.4% | 65.2% | 64.4% | 77.7% | 75.3% | 68.6% |
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Zhang, T.; Li, C.; Li, P.; Peng, Y.; Kang, X.; Jiang, C.; Li, F.; Zhu, X.; Yao, D.; Biswal, B.; et al. Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset. Entropy 2020, 22, 893. https://doi.org/10.3390/e22080893
Zhang T, Li C, Li P, Peng Y, Kang X, Jiang C, Li F, Zhu X, Yao D, Biswal B, et al. Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset. Entropy. 2020; 22(8):893. https://doi.org/10.3390/e22080893
Chicago/Turabian StyleZhang, Tao, Cunbo Li, Peiyang Li, Yueheng Peng, Xiaodong Kang, Chenyang Jiang, Fali Li, Xuyang Zhu, Dezhong Yao, Bharat Biswal, and et al. 2020. "Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset" Entropy 22, no. 8: 893. https://doi.org/10.3390/e22080893
APA StyleZhang, T., Li, C., Li, P., Peng, Y., Kang, X., Jiang, C., Li, F., Zhu, X., Yao, D., Biswal, B., & Xu, P. (2020). Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset. Entropy, 22(8), 893. https://doi.org/10.3390/e22080893