Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE

1998

Earlier work this paper cites.

S. M. Smith, M. Jenkinson, M. W. Woolrich, C. F. Beckmann, T. E. Behrens, H. Johansen-Berg, P. R. Bannister, M. De Luca, I. Drobnjak, D. E. Flitney, et al

2004

Earlier work this paper cites.

E. E. Tripoliti, D. I. Fotiadis, and M. Argyropoulou, “A supervised method to assist the diagnosis and classification of the status of alzheimer’s disease using data from an fmri experiment,” in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

2008

Earlier work this paper cites.

I. Arel, D. C. Rose, and T. P. Karnowski, “Deep machine learning-a new frontier in artificial intelligence research [research frontier],” Computational Intelligence Magazine, IEEE

2010

Earlier work this paper cites.

P. Vemuri, D. T. Jones, and C. R. Jack Jr, “Resting state functional mri in alzheimer’s disease,” Alzheimer’s research & therapy

2012

Earlier work this paper cites.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the ACM International Conference on Multimedia

2014

Cited alongside, same era.

S. Sarraf, C. Saverino, H. Ghaderi, and J. Anderson, “Brain network extraction from probabilistic ica using functional magnetic resonance images and advanced template matching techniques,” in Electrical and Computer Engineering (CCECE), 2014 IEEE 27th Canadian Conference on

2014

Cited alongside, same era.

S. Sarraf, E. Marzbanrad, and H. Mobedi, “Mathematical modeling for predicting betamethasone profile and burst release from in situ forming systems based on plga,” in Electrical and Computer Engineering (CCECE), 2014 IEEE 27th Canadian Conference on

2014

Cited alongside, same era.

S. C. Strother, S. Sarraf, and C. Grady, “A hierarchy of cognitive brain networks revealed by multivariate performance metrics,” in Signals, Systems and Computers, 2014 48th Asilomar Conference on

X. Zhang, B. Hu, X. Ma, and L. Xu, “Resting-state whole-brain functional connectivity networks for mci classification using l2-regularized logistic regression,” NanoBioscience, IEEE Transactions on

Original

2015

Later among the works it cites.

C. Grady, S. Sarraf, C. Saverino, and K. Campbell, “Age differences in the functional interactions among the default, frontoparietal control and dorsal attention networks,” Neurobiology of Aging

2016

Closest in time.

S. Sarraf and J. Sun, “Functional brain imaging: A comprehensive survey,” arXiv preprint arXiv:1602.02225

Original

2016

Closest in time.

S. Sarraf and A. M. Golestani, “A robust and adaptive decision-making algorithm for detecting brain networks using functional mri within the spatial and frequency domain,” in The IEEE International Conference on Biomedical and Health Informatics (BHI)

2016

Closest in time.