Abstract
The fast and accurate diagnosis of Alzheimer’s Disease (AD) plays a significant part in patient care, especially at the early stage. The main difficulty lies in the three-class classification problem with AD, Mild Cognitive Impairment (MCI) and Normal Cohort (NC) subjects, due to the high similarity on brain patterns and image intensities between AD and MCI’s Magnetic Resonance Imaging (MRI). So far, many studies have explored and applied various techniques, including static analysis methods and machine learning algorithms for Computer Aided Diagnosis (CAD) of AD. But there is still lack of a balance between the speed and accuracy of existing techniques, i.e., fast methods are not accurate while accurate algorithms are not fast enough. This paper proposes a new deep learning architecture to achieve the tradeoff between the speed and accuracy of AD diagnosis, which predicts three binary and one three-class classification in a unified architecture named 3D fine-tuning convolutional neural network (3D-FCNN). Experiments on the standard Alzheimer’s disease Neuroimaging Initiative (ADNI) MRI dataset indicated that the proposed 3D-FCNN model is superior to conventional classifiers both in accuracy and robustness. In particular, the achieved binary classification accuracies are 96.81% and AUC of 0.98 for AD/NC, 88.43% and AUC of 0.91 for AD/MCI, 92.62% and AUC of 0.94 for MCI/NC. More importantly, the three-class classification for AD/MCI/NC achieves the accuracy of 91.32%, outperforming several state-of-the-art approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Prince, M., Bryce, R., Albanese, E., et al.: The global prevalence of dementia: a systematic review and metaanalysis. Alzheimer’s Dement.: J. Alzheimer’s Assoc. 9(1), 63–75 (2013)
Klöppel, S., Stonnington, C.M., Barnes, J., et al.: Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method. Brain 131(11), 2969–2974 (2008)
Zhang, D., Wang, Y., Zhou, L., et al.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3), 856 (2011)
Welch’s, U.: Alzheimer’s disease detection in brain magnetic resonance images using multiscale fractal analysis. ISRN Radiol. 2013(41), 627303 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, vol. abs/1409.1556 (2014)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778. IEEE (2016)
Gupta, A., Maida, A.S., Ayhan, M.: Natural image bases to represent neuroimaging data. In: International Conference on Machine Learning, pp. 987–994 (2013)
Liu, S., Liu, S., Cai, W., et al.: Early diagnosis of Alzheimer’s disease with deep learning. In: IEEE International Symposium on Biomedical Imaging, pp. 1015–1018. IEEE (2014)
Ortiz, A., Munilla, J., Gorriz, J.M., Ramírez, J.: Ensembles of deep learning architectures for the early diagnosis of Alzheimer’s disease. Int. J. Neural Syst. 26(7), 1650025 (2016)
Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprint arXiv:1502.02506 (2015)
Hosseiniasl, E., Ghazal, M., Mahmoud, A., et al.: Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network. Front. Biosci. 23, 584–596 (2018)
Ashburner, J., Friston, K.J.: Unified segmentation. Neuroimage 26(3), 839–851 (2005)
Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146–2153. IEEE (2009)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640 (2017)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2015)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE (2016)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on Machine Learning, pp. 807–814. Omnipress (2010)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, 448–456 (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Chetelat, G., Desgranges, B., De La Sayette, V., et al.: Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. NeuroReport 13(15), 1939–1943 (2002)
Misra, C., Fan, Y., Davatzikos, C.: Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 44(4), 1415–1422 (2009)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3859–3869 (2017)
Acknowledgements
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tang, H., Yao, E., Tan, G., Guo, X. (2018). A Fast and Accurate 3D Fine-Tuning Convolutional Neural Network for Alzheimer’s Disease Diagnosis. In: Zhou, ZH., Yang, Q., Gao, Y., Zheng, Y. (eds) Artificial Intelligence. ICAI 2018. Communications in Computer and Information Science, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-13-2122-1_9
Download citation
DOI: https://doi.org/10.1007/978-981-13-2122-1_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2121-4
Online ISBN: 978-981-13-2122-1
eBook Packages: Computer ScienceComputer Science (R0)