![](/images/graphics-bg.png)
A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification
المؤلفون المشاركون
Dong, Shoubin
Hu, Jinlong
Kuang, Yuezhen
Liao, Bin
Cao, Lijie
Li, Ping
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-12-31
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Deep learning models have been successfully applied to the analysis of various functional MRI data.
Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics.
In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data.
The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks.
We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data.
We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP).
The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hu, Jinlong& Kuang, Yuezhen& Liao, Bin& Cao, Lijie& Dong, Shoubin& Li, Ping. 2019. A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129480
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hu, Jinlong…[et al.]. A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1129480
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hu, Jinlong& Kuang, Yuezhen& Liao, Bin& Cao, Lijie& Dong, Shoubin& Li, Ping. A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129480
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129480
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)