Abstract
Magnetic Resonance Imaging (MRI) is a critical medical diagnostic tool that assists experts in precisely identifying lesions. However, due to its high-dimensional nature, it requires substantial storage resources. If only one MRI slice were to be used, a significant amount of information might be lost. To address these issues, we propose segmenting 3D MRI data and training these slices separately. We propose a new Multi-view Learning neural network based on ResNet and an Attention mechanism, called MvRNA. ResNet18 is selected as the backbone network, and the Squeeze-and-Excitation network is applied between blocks to extract features from slices. Additionally, we propose a new BWH (Basic Block with Hybrid Dilated Convolution) module to capture a broader range of receptive fields, thus acquiring additional spatial features. We obtained data from Parkinson’s Progression Markers Initiative (PPMI) and applied our method to distinguish between Healthy Control, Prodromal, and Parkinson’s disease patients. The experimental results demonstrate that our method achieved an accuracy of 81.84%.
L. Chen and Y. Zhou contribute equally to this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dmitriev, K., Marino, J., Baker, K., Kaufman, A.E.: Visual analytics of a computer-aided diagnosis system for pancreatic lesions. IEEE Trans. Visual Comput. Graphics 27(3), 2174–2185 (2021). https://doi.org/10.1109/TVCG.2019.2947037
Han, L., Kamdar, M.R.: MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks. In: Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, vol. 23, p. 331 (2018)
Tang, Z., Xu, Y., Jin, L., Aibaidula, A., Shen, D.: Deep learning of imaging phenotype and genotype for predicting overall survival time of glioblastoma patients. IEEE Trans. Med. Imaging PP(99), 1 (2020)
Thuseethan, S., Rajasegarar, S., Yearwood, J.: Detecting micro-expression intensity changes from videos based on hybrid deep CNN. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) PAKDD 2019. LNCS (LNAI), vol. 11441, pp. 387–399. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16142-2_30
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. Computer Science (2013)
Zhou, W., Wang, H., Yang, Y.: Consensus Graph Learning for Incomplete Multi-view Clustering. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) PAKDD 2019. LNCS (LNAI), vol. 11439, pp. 529–540. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16148-4_41
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018). https://doi.org/10.1109/CVPR.2018.00745
Wang, P., et al.: Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1451–1460 (2018). https://doi.org/10.1109/WACV.2018.00163
Marek, K., Jennings, D., Lasch, S., Siderowf, A., Taylor, P.: The Parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95(4), 629–635 (2011)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, no. 2 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv e-prints (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594
Zhu, S.: Early diagnosis of Parkinsons disease by analyzing magnetic resonance imaging brain scans and patient characteristics (2022)
Erdaş, Ç.B., Sümer, E.: A deep learning method to detect Parkinson’s disease from MRI slices. SN Comput. Sci. 3(2), 1–7 (2022). https://doi.org/10.1007/s42979-022-01018-y
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Chhabra, M., Kumar, R.: An efficient ResNet-50 based intelligent deep learning model to predict pneumonia from medical images. In: 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), pp. 1714–1721 (2022). https://doi.org/10.1109/ICSCDS53736.2022.9760995
Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2Net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652–662 (2021). https://doi.org/10.1109/TPAMI.2019.2938758
Wang, Y., et al.: SwinMM: masked Multi-view with Swin transformers for 3D medical image segmentation. In: Greenspan, H., et al. (ed.) MICCAI 2023. MICCAI 2023. LNCS, vol. 14222. Springer, Cham. (2023). https://doi.org/10.1007/978-3-031-43898-1_47
Liu, D., Gao, Y., Zhangli, Q., Yan, Z., Zhou, M., Metaxas, D.: Transfusion: multi-view divergent fusion for medical image segmentation with transformers. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16443-9_47
Liu, J., Pan, Y., Wu, F.X., Wang, J.: Enhancing the feature representation of multi-modal MRI data by combining multi-view information for MCI classification. Neurocomputing 400, 322–332 (2020)
Xue, Z., Zhang, T., Lin, L.: Progress prediction of Parkinson’s disease based on graph wavelet transform and attention weighted random forest. Expert Syst. Appl. 203, 117483 (2022)
Zhang, Y., Lei, H., Huang, Z., Li, Z., Liu, C.M., Lei, B.: Parkinson’s disease classification with self-supervised learning and attention mechanism. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp. 4601–4607 (2022). https://doi.org/10.1109/ICPR56361.2022.9956213
Park, J., Woo, S., Lee, J.Y., Kweon, I.S.: BAM: Bottleneck attention module (2018)
Zhang, Q.L., Yang, Y.B.: SA-Net: shuffle attention for deep convolutional neural networks. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2235–2239 (2021). https://doi.org/10.1109/ICASSP39728.2021.9414568
Liu, M., Yang, J.: Image classification of brain tumor based on channel attention mechanism. J. Phys: Conf. Ser. 2035(1), 012029 (2021). https://doi.org/10.1088/1742-6596/2035/1/012029
Zhou, Q., et al.: Grading of hepatocellular carcinoma using 3d SE-DenseNet in dynamic enhanced MR images. Comput. Biol. Med. 107, 47–57 (2019). https://doi.org/10.1016/j.compbiomed.2019.01.026
Linqi, J., Chunyu, N., Jingyang, L.: Glioma classification framework based on SE-ResNeXt network and its optimization. IET Image Processing 2(16), 596–605 (2022)
Luo, M., et al.: A multi-granularity convolutional neural network model with temporal information and attention mechanism for efficient diabetes medical cost prediction. Comput. Biol. Med. 151, 106246 (2022). https://doi.org/10.1016/j.compbiomed.2022.106246
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)
Jiang, W., Liu, M., Peng, Y., Wu, L., Wang, Y.: HDCB-Net: a neural network with the hybrid dilated convolution for pixel-level crack detection on concrete bridges. IEEE Trans. Industr. Inf. 17(8), 5485–5494 (2021). https://doi.org/10.1109/TII.2020.3033170
Zhao, X., et al.: D2A U-NET: automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution. Comput. Biol. Med. 135, 104526 (2021)
Xiao, Y., Fonov, V., Chakravarty, M.M., Beriault, S., Collins, D.L.: A dataset of multi-contrast population-averaged brain MRI atlases of a Parkinsons disease cohort. Data Brief 12(C), 370–379 (2017)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61976247), the Key Research and Development Program in Sichuan Province of China (No. 2023YFS0404), the Central guiding Local Science and Technology Development Fund (No. 23ZYZYTS0189), the Fundamental Research Funds for the Central Universities (Nos. 2682022KJ045 and 2682023ZTPY081), and the Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province (No. DRN2203).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, L., Zhou, Y., Zhang, X., Zhang, Z., Zheng, H. (2024). MvRNA: A New Multi-view Deep Neural Network for Predicting Parkinson’s Disease. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14646. Springer, Singapore. https://doi.org/10.1007/978-981-97-2253-2_8
Download citation
DOI: https://doi.org/10.1007/978-981-97-2253-2_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2252-5
Online ISBN: 978-981-97-2253-2
eBook Packages: Computer ScienceComputer Science (R0)