A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks
المؤلفون المشاركون
Yang, Hao
Liu, Yunfei
Mo, Xian
Liu, Yan
Zhang, Junran
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-21
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Background.
As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer’s disease.
Method.
In this work, we propose a convolutional neural network (CNN) based classification method to help classify diabetes by distinguishing the brains with abnormal functions from the normal ones on resting-state functional magnetic resonance imaging (rs-fMRI).
The proposed classification model is based on the Inception-v4-Residual convolutional neural network architecture.
In our workflow, the original rs-fMRI data are first mapped to generate amplitude of low-frequency fluctuation (ALFF) images and then fed into the CNN model to get the classification result to indicate the potential existence of DM.
Result.
We validate our method on a realistic clinical rs-fMRI dataset, and the achieved average accuracy is 89.95% in fivefold cross-validation.
Our model achieves a 0.8690 AUC with 77.50% and 77.51% sensitivity and specificity using our local dataset, respectively.
Conclusion.
It has the potential to become a novel clinical preliminary screening tool that provides help for the classification of different categories based on functional brain alteration caused by diabetes, benefiting from its accuracy and robustness, as well as efficiency and patient friendliness.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Yunfei& Mo, Xian& Yang, Hao& Liu, Yan& Zhang, Junran. 2020. A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1193605
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Yunfei…[et al.]. A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1193605
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Yunfei& Mo, Xian& Yang, Hao& Liu, Yan& Zhang, Junran. A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1193605
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1193605
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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