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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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General Research Article

Diagnosis of Alzheimer's Disease Based on Deeply-Fused Nets

Author(s): Chang Zu Chen, Qi Wu, Zuo Yong Li , Lei Xiao and Zhong Yi Hu*

Volume 24, Issue 6, 2021

Published on: 25 August, 2020

Page: [781 - 789] Pages: 9

DOI: 10.2174/1386207323666200825092649

Price: $65

Abstract

Aim and Objective: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the development of deep learning, impressive progress has also been made in the automatic diagnosis of AD. Most existing studies on automatic diagnosis are concerned with a single base network, whose accuracy for disease diagnosis still needs to be improved. This study was undertaken to propose a method to improve the accuracy of the automatic diagnosis of AD.

Materials and Methods: MRI image data from the Alzheimer’s Disease Neuroimaging Initiative were used to train a deep learning model to achieve a computer-aided diagnosis of Alzheimer's disease. The data consisted of 138 with AD, 280 with mild cognitive impairment, and 138 normal controls. Here, a new deeply-fused net is proposed, which combines several deep convolutional neural networks, thereby avoiding the error of a single base network and increasing the classification accuracy and generalization capacity.

Results: Experiments show that when differentiating between patients with AD, mild cognitive impairment, and normal controls on a subset of the ADNI database without data leakage, the new architecture improves the accuracy by about 4 percentage points as compared to a single standard based network.

Conclusion: This new approach exhibits better performance, but there is still much to be done before its clinical application. In the future, greater research effort will be devoted to improving the performance of the deeply-fused net.

Keywords: Deep convolutional network, Alzheimer's disease, fused net, single standard base network, diagnosis, accuracy of automatic diagnosis.

« Previous Next »
[1]
Dementia World Health Organization, https://www.w-ho.int/news-room/fact-sheets/detail/dementia
[2]
Ribeiro, F.F.; Mendonca Junior, F.J.B.; Ghasemi, J.B.; Ishiki, H.M.; Scotti, M.T.; Scotti, L. Docking of natural products against neurodegenera-tive diseases: general concepts. Comb. Chem. High Throughput Screen., 2018, 21(3), 152-160.
[http://dx.doi.org/10.2174/1386207321666180313130314] [PMID: 29532756]
[3]
Kong, W.; Mou, X.; Di, B.; Deng, J.; Zhong, R.; Wang, S. Dysregulated pathway identification of alzheimer’s disease based on internal correlation analysis of genes and pathways. Comb. Chem. High Throughput Screen., 2017, 20(10), 896-904.
[http://dx.doi.org/10.2174/1386207320666171121112235] [PMID: 29165066]
[4]
Jagust, W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat. Rev. Neurosci., 2018, 19(11), 687-700.
[http://dx.doi.org/10.1038/s41583-018-0067-3] [PMID: 30266970]
[5]
Fung, Y.R.; Guan, Z.; Kumar, R.; Wu, J.Y.; Fiterau, M. Alzheimer’s disease brain mri classification: challenges and insights. International Joint Conference on Artificial Intelligence, 2019.
[6]
LeCun, Y; Bengio, Y; Hinton, G. Deep learning nature, 2015, 521(7553), 436-444.
[7]
Wei, L.; Su, R.; Wang, B.; Li, X.; Zou, Q.; Gao, X. Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites. Neurocomputing, 2019, 324, 3-9.
[http://dx.doi.org/10.1016/j.neucom.2018.04.082]
[8]
Wei, L.; Zhou, C.; Chen, H.; Song, J.; Su, R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics, 2018, 34(23), 4007-4016.
[http://dx.doi.org/10.1093/bioinformatics/bty451] [PMID: 29868903]
[9]
Wei, L.; Wan, S.; Guo, J.; Wong, K.K.L. A novel hierarchical selective ensemble classifier with bioinformatics application. Artif. Intell. Med., 2017, 83, 82-90.
[http://dx.doi.org/10.1016/j.artmed.2017.02.005] [PMID: 28245947]
[10]
Liu, S.; Yadav, C.; Fernandez-Granda, C.; Razavian, N. On the design of convolutional neural networks for automatic detection of Alzheimer’s disease. Annual Conference on Neural Information Processing Systems, 2019.
[11]
Polikar, R. Ensemble learning. Ensemble machine learning; Springer: Boston, MA, 2012, pp. 1-34.
[http://dx.doi.org/10.1007/978-1-4419-9326-7_1]
[12]
Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015.
[13]
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[14]
Risacher, S.L.; Saykin, A.J.; West, J.D.; Shen, L.; Firpi, H.A.; McDonald, B.C. Alzheimer’s Disease Neuroimaging Initiative (ADNI). Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr. Alzheimer Res., 2009, 6(4), 347-361.
[http://dx.doi.org/10.2174/156720509788929273] [PMID: 19689234]
[15]
Yang, W.; Lui, R.L.; Gao, J.H.; Chan, T.F.; Yau, S.T.; Sperling, R.A.; Huang, X. Independent component analysis-based classification of Alzheimer’s disease MRI data. J. Alzheimers Dis., 2011, 24(4), 775-783.
[http://dx.doi.org/10.3233/JAD-2011-101371] [PMID: 21321398]
[16]
Liu, S.; Song, Y.; Cai, W.; Pujol, S.; Kikinis, R.; Wang, X.; Feng, D. Multifold Bayesian Kernelization in Alzheimer’s Diagnosis; Advanced Information Systems Engineering Lecture Notes in Computer Science, 2013, pp. 303-310.
[17]
Zhang, D.; Wang, Y.; Zhou, L.; Yuan, H.; Shen, D. Alzheimer’s disease neuroimaging initiative. Multimodal classification of alzheimer’s disease and mild cognitive impairment. Neuroimage, 2011, 55(3), 856-867.
[http://dx.doi.org/10.1016/j.neuroimage.2011.01.008] [PMID: 21236349]
[18]
Zhang, D.; Zu, C.; Jie, B.; Ye, T. Multi-modality Feature Learning in Diagnoses of Alzheimer’s Disease.Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging; Springer: Cham, 2018, pp. 3-30.
[http://dx.doi.org/10.1007/978-3-319-68843-5_1]
[19]
Billones, C.D.; Demetria, O.J.L.D.; Hostallero, D.E.D.; Naval, P.C. DemNet: A Convolutional Neural Network for the Detection of Alzheimers Disease and Mild Cognitive Impairment. 2016 IEEE Region 10 Conference; TENCON, 2016.
[20]
Hon, M.; Khan, N.M. Towards Alzheimer’s Disease Classification through Transfer Learning. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017.
[http://dx.doi.org/10.1109/BIBM.2017.8217822]
[21]
Szegedy, C.; Ioffe, S.; Vanhoucke, V.; Alemi, A.A. Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-first AAAI conference on artificial intelligence, 2017.
[22]
Jain, R.; Jain, N.; Aggarwal, A.; Hemanth, D.J. Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cogn. Syst. Res., 2019, 57, 147-159.
[http://dx.doi.org/10.1016/j.cogsys.2018.12.015]
[23]
Wang, S.; Wang, H.; Shen, Y.; Wang, X. Automatic Recognition of Mild Cognitive Impairment and Alzheimers Disease Using Ensemble Based 3D Densely Connected Convolutional Networks 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018.
[24]
Ashburner, J.; Friston, K.J. Voxel-based morphometry--the methods. Neuroimage, 2000, 11(6 Pt 1), 805-821.
[http://dx.doi.org/10.1006/nimg.2000.0582] [PMID: 10860804]
[25]
Shen, L.; Lin, Z.; Huang, Q. Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks. Computer Vision – ECCV 2016. Lect. Notes Comput. Sci., 2016, 467-482.
[http://dx.doi.org/10.1007/978-3-319-46478-7_29]
[26]
Valliani, A.; Soni, A. Deep Residual Nets for Improved Alzheimers Diagnosis. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics - ACM-BCB 17, 2017.
[27]
Hinton, G.E.; Srivastava, N.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R.R. Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci., 2012.

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