Abstract
Developing a correct diagnosis of Alzheimer’s disease (AD) is a challenging task. Positron emission tomography (PET) is a good method to help doctors assist in the diagnosis of AD. In recent years, artificial intelligence methods such as machine learning have been widely used in image analysis and judgment and medical auxiliary diagnosis. The current methods are mainly to manually extract image features from medical images and then train classifiers to judge AD, or use deep learning, neural networks for end-to-end AD classification, most methods only use a single-mode method, and the classification effect is limited. This paper proposes a multi-mode network structure based on CNN to classify and diagnose AD. The network is mainly divided into three parts: CNN-based multi-scale deep-level feature extraction module, image texture feature extraction module, and SVM-based feature integration classification module. The network fully combines the advantages of the two modes of manual feature extraction and neural network. Compared with single mode feature extraction, this method has higher accuracy and has a good performance on the classification and diagnosis of AD.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Pan, X., Adel, M., Fossati, C., Gaidon, T., Guedj, E.: Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer’s Disease. IEEE J. Biomed. Health Inf. 23, 1499–1506 (2019)
Reitz, C., Brayne, C., Mayeux, R.: Epidemiology of alzheimer disease. Nat. Rev. Neurol. 7, 137–152 (2011)
Wortmann, M.: Dementia: a global health priority - highlights from an ADI and world health organization report. Alzheimer’s Res. Ther. 4, 40 (2012)
Cheng, D., Liu, M.: Classification of Alzheimer’s disease by cascaded convolutional neural networks using PET Images. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 106–113. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_13
Garali, I., Adel, M., Bourennane, S., Guedj, E.: Region-based brain selection and classification on pet images for Alzheimer’s disease computer aided diagnosis. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 1473–1477. Quebec City (2015)
Serag, A., Wenzel, F., Thiele, F., Buchert, R., Young, S.: Optimal feature selection for automated classification of FDG-PET in patients with sus-pected dementia. In: Medical Imaging 2009, Florida, United States (2009)
Dhungel, N., Carneiro, G., Bradley, A.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal. 37, 114–128 (2017)
Setio, A., et al.: Pulmonary Nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Medi. Imaging 35, 1160–1169 (2016)
Xu, L., Wu, X., Chen, K., Yao, L.: Multi-modality sparse representation-based classification for Alzheimer’s disease and mild cognitive impairment. Comput. Methods Programs Biomed. 122, 182–190 (2015)
Xue, Y., Zhang, R., Deng, Y., Chen, K., Jiang, T.: A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS ONE 12, e0178992 (2017)
Garali, I., Adel, M., Bourennane, S., Guedj, E.: Region-based brain selection and classification on pet images for Alzheimer’s disease computer aided diagnosis. In: IEEE International Conference on Image Processing, pp. 1473–1477 (2015)
Shen, L., Xia, Y., Cai, T.W., Feng, D.D.: Semi-supervised manifold learning with affinity regularization for Alzheimer’s disease identification. In: International Conference of the IEEE EMBS, p. 2251 (2015)
Silveira, M., Marques, J.: Boosting Alzheimer disease diagnosis using PET images. In: International Conference on Pattern Recognition, pp. 2556–2559 (2010)
Cabral, C., Silveira, M.: Classification of Alzheimer’s disease from FDG-PET images using favourite class ensembles. In: Engineering in Medicine and Biology Society, pp. 2477–2480. IEEE (2013)
Vu, T., Yang, H., Nguyen, V., Oh, A., Kim, M.: Multimodal learning using convolution neural network and sparse autoencoder. In: IEEE International Conference on Big Data and Smart Computing, pp. 13–16. Jeju, South Korea (2017)
Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoustics Speech Signal Process. 29, 1153–1160 (1981)
Kong, F.: Image retrieval using both color and texture features. In: 2009 International Conference on Machine Learning and Cybernetics, pp. 2228–2232. Hebei, China (2009)
Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cyber. SMC-3, 610–621 (1973)
Haralick, R.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979)
Nikoo, H., Talebi, H., Mirzaei, A.: A supervised method for determining displacement of gray level co-occurrence matrix. In: 7th Iranian Conference on Machine Vision and Image Processing, pp. 1–5, 16–17. (2011)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, S., Huang, H. (2021). Diagnosis Method of Alzheimer’s Disease in PET Image Based on CNN Multi-mode Network. In: Zu, Q., Tang, Y., Mladenović, V. (eds) Human Centered Computing. HCC 2020. Lecture Notes in Computer Science(), vol 12634. Springer, Cham. https://doi.org/10.1007/978-3-030-70626-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-70626-5_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70625-8
Online ISBN: 978-3-030-70626-5
eBook Packages: Computer ScienceComputer Science (R0)