Combining convolutional and recurrent neural networks for Alzheimer's disease diagnosis using PET images
Abstract
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder which often happens in people aged more than 65 years old. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Positrons Emission Tomography (PET) is a functional molecular imaging modality, which proves to be a powerful tool to help understand the brain changes related to AD. Most existing methods extract the handcraft features from images, and then train a classifier to distinguish AD from other groups. The success of these computer-aided diagnosis methods highly depends on the image preprocessing, including rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNN), which learns the features of 3D PET images by decomposing the 3D image into a sequence of 2D slices. In this framework, the hierarchical 2D CNNs are built to capture the intra-slice features while the gated recurrent unit (GRU) of RNN is used to extract the inter-slice features for final classification. No rigid image registration and segmentation are required for PET images. Our method is evaluated on the baseline PET images from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an AUC of 95.28% for classification of AD vs. NC and 83.90% for classification of MCI vs. NC, respectively, demonstrating the promising classification performance.
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CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis.
“Recently, the CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis [51,52,53,54,55,56,57,58].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [53]
Cheng et al. achieved 0.9528 AUC for AD vs. NC on ADNI using a sequence of 2D slice groups from 3D PET.
“| Cheng et al. [53] | a sequence of 2D slice groups from 3D PET | PET | ADNI | 0.9528 (AUC: AD vs. NC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [53]
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317 · see citing works
Beyond short snippets: Deep networks for video classification
Ng, J. Y.-H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. D. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299101 · Cited by 2,121 · see citing works
Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5590 · Cited by 1,005 · see citing works
A Theoretically Grounded Application of Dropout in Recurrent Neural NetworksPreprint
Gal, Y., & Zoubin Ghahramani, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.05287 · Cited by 765 · see citing works
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
Initiative, T. A. D. N., Suk, H., Lee, S., & Shen, D. G. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0687-3 · Cited by 523 · see citing works
Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis
Li, R., Zhang, W., Suk, H., Wang, L., Jiang, L., Shen, D. G., & Ji, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_39 · Cited by 480 · see citing works
Alzheimer's disease diagnostics by adaptation of 3D convolutional network
Hosseini-Asl, E., Keynton, R., & El-Baz, A. S. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532332 · Cited by 331 · see citing works
Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition
Wang, X., Gao, L., Song, J., & Shen, H. T. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2611485 · Cited by 264 · see citing works
Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation
Tseng, K.-L., Lin, Y.-L., Hsu, W. H., & Huang, C.-Y. R. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.398 · Cited by 190 · see citing works
(2015) · arXiv (Cornell University) · Cited by 182 · see citing works
Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease
Gray, K. R., Wolz, R., Heckemann, R. A., Aljabar, P., Hammers, A., & Rueckert, D. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.071 · Cited by 177 · see citing works
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Chen, J., Yang, L., Zhang, Y., Alber, M., & Chen, D. Z. (2016) · neural information processing systems · Cited by 113 · see citing works
Boosting Alzheimer Disease Diagnosis Using PET Images
Silveira, M., & Marques, J. S. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.626 · Cited by 67 · see citing works
Multi-view Classification for Identification of Alzheimer’s Disease
Zhu, X., Suk, H., Zhu, Y., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24888-2_31 · Cited by 42 · see citing works
Chen, C., & Ross, A. A. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.06.021 · Cited by 33 · see citing works
Regional analysis of FDG-PET for use in the classification of Alzheimer'S Disease
Gray, K. R., Wolz, R., Shiva Keihaninejad, A., Heckemann, R. A., Aljabar, P., Hammers, A., & Rueckert, D. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872589 · Cited by 31 · see citing works
Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical SegmentationPreprint
Tseng, K.-L., Lin, Y.-L., Hsu, W. H., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.07754 · Cited by 26 · see citing works
Garali, I., Adel, M., Bourennane, S., et al. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351045 · Cited by 22 · see citing works
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
- Authors
- Cheng, D., & Liu, M.
- Affiliations
- Shanghai Jiao Tong University (CN)
- ORCID
- Liu, Manhua
- Venue
- 2017 IEEE International Conference on Imaging Systems and Techniques (IST)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1-5
- OpenAlex
- W2783054603
- Cited by
- 49
- References
- 21
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 12 · 2022: 8 · 2021: 10 · 2020: 4 · 2019: 5 · 2018: 1
- FWCI
- 7.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Imaging Systems and Techniques (IST) (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease diagnosis, PET images, convolutional neural networks, recurrent neural networks, gated recurrent unit, mild cognitive impairment, computer-aided diagnosis, ADNI database
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