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Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

التفاصيل البيبلوغرافية
العنوان: Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data
المؤلفون: Ke Niu, Jiayang Guo, Yijie Pan, Xin Gao, Xueping Peng, Ning Li, Hailong Li
المصدر: Complexity, Vol 2020 (2020)
بيانات النشر: Wiley, 2020.
سنة النشر: 2020
المجموعة: LCC:Electronic computers. Computer science
مصطلحات موضوعية: Electronic computers. Computer science, QA75.5-76.95
الوصف: Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1076-2787
1099-0526
Relation: https://doaj.org/toc/1076-2787; https://doaj.org/toc/1099-0526
DOI: 10.1155/2020/1357853
URL الوصول: https://doaj.org/article/83c3bab270ee467f9f762d5ecc4ac32d
رقم الأكسشن: edsdoj.83c3bab270ee467f9f762d5ecc4ac32d
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:10762787
10990526
DOI:10.1155/2020/1357853