City Research Online

Abstract

With recent advancements in machine learning, the research community has made tremendous advances towards the classification of neurological disorders from time-series functional MRI signals. However, existing classification techniques rely on hand-crafted features and classical machine learning models. In this paper, we propose an end-to-end model that utilizes the representation learning capability of deep learning to classify a neurological disorder from fMRI data. The proposed DeepFMRI model is comprised of three networks, namely (1) a feature extractor, (2) a similarity network, and (3) a classification network. The model takes fMRI raw time-series signals as input and outputs the predicted labels; and is trained end-to-end using back-propagation. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative model outperforms previous state-of-the-art.

Publication Type:	Article
Additional Information:	Published as: Riaz, A., Asad, M., Al-Arif, S. M., Alonso, E. , Dima, D. , Corr, P. J. and Slabaugh, G.G. (2018). DeepFMRI: And End-to-End Deep Network for Classification of FRMI Data. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1419-1422. doi: 10.1109/ISBI.2018.8363838. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	Deep learning, end-to-end model, fMRI classification
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Health & Psychological Sciences > Psychology School of Science & Technology > Computer Science

Export

Downloads