Abstract
Alzheimer’s Disease (AD) is a serious public health issue that affects elderly people. According to the World Health Organization (WHO), nearly 100,000 people suffer from it in Algeria. Therefore, early diagnosis is vital for the patient’s treatment. In this paper, to improve the precision of early diagnosis, MRI images are employed to extract hidden features using a variety of transfer learning strategies on convolutional neural network models with different fine-tuning levels. The learned features are transferred from a large dataset of natural images to a small dataset of AD MRI images. For this purpose, progressive transfer learning is proposed, using the brain cancer MRI dataset as an intermediate to maintain both general features and domain-specific features relevant to the target domain. Accordingly, our tests showed that the performance results of the proposed approach produce a high accuracy of 99.22%, 98.90%, 98.28%, and 99.37% using respectively the VGG16, MobileNetV2, Xception and Inception models. These results demonstrate that, even with a highly diverse database like ImageNet, selecting the appropriate architecture and level of fine-tuning yields an improved adaptability and specialization of the pre-trained model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lama, R.K., Gwak, J., Park, J.-S., Lee, S.-W.: Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and pca features. J. Healthc. Eng. 2017, 1–11 (2017). https://doi.org/10.1155/2017/5485080
Prepared by: Office of Communications and Public Liaison: Neurological Diagnostic Tests and Procedures Fact Sheet. Neurological Diagnostic Tests and Procedures”, NINDS. NIH Publication No. 19-NS-5380. 10 Apr 2019
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449–459 (2017)
Arafa, D.A., Moustafa, H.E.D., Ali-Eldin, A.M., Ali, H.A.: Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey. Multimedia Tools Appl. 81, 23735–23776 (2022)
Ding, Y., et al.: A deep learningmodel to predict a diagnosis of Alzheimer disease by using 18f-fdg pet of the brain. Radiology 290(2), 456–464 (2019)
Hosseini-Asl, E., Gimel’farb, G., El-Baz, A.: Alzheimer’s disease diagnostics by a deeply supervised adaptable 3d convolutional network. arXiv preprint arXiv:1607.00556 (2016)
Zamir, A.R., Sax, A., Shen, W., Guibas, L.J., Malik, J., Savarese, S.: Taskonomy: Disentangling task transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3712–3722 (2018)
Zhang, L., Wang, M., Liu, M., Zhang, D.: A survey on deep learning for neuroimaging-based brain disorder analysis. Front. Neurosci. 14, 779 (2020)
Acharya, H., Mehta, R. and Kumar Singh, D.: Alzheimer disease classification using transfer learning. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1503–1508 (2021). https://doi.org/10.1109/ICCMC51019.2021.9418294
Taeho, J., Kwangsik, N., Andrew, J.S.: Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front. Aging Neurosci. 11, 220 (2019). https://doi.org/10.3389/fnagi.2019.00220
Agarwal, D., Marques, G., de la Torre-Díez, I., Franco Martin, M.A., García Zapiraín, B., Martín Rodríguez, F.: Transfer learning for Alzheimer’s disease through neuro-imaging biomarkers: a systematic review. Sensors 21(21), 7259 (2021)
Hon, M., Khan, N.M.: Towards Alzheimer’s disease classification through transfer learning. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1166–1169. IEEE (2017)
Khan, N.M., Abraham, N., Hon, M.: Transfer learning with intelligent training data selection for prediction of Alzheimer’s disease. IEEE Access 7, 72726–72735 (2019)
Oktavian, M.W., Yudistira, N., Ridok, A.: Classification of alzheimer’s disease using the convolutional neural network (CNN) with transfer learning and weighted loss. arXiv preprint arXiv:2207.01584 (2022)
Zhu, X., et al.: Transfer learning for cognitive reserve quantification. NeuroImage 258, 119353 (2022). https://doi.org/10.1016/j.neuroimage.2022.119353
Zhao, X., Zhao, X.M.: Deep learning of brain magnetic resonance images: a brief review. Methods 192, 131–140 (2021)
Helaly, H.A., Badawy, M., Haikal, A.Y.: Deep learning approach for early detection of Alzheimer’s disease. Cognitive Comput. 1–17 (2021)
Tan, X., et al.: Localized instance fusion of MRI data of Alzheimer’s disease for classification based on instance transfer ensemble learning. Biomed. Eng. Online 17(1), 1–17 (2018)
Kandel, I., Castelli, M.: Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Appl. Sci. 10(6), 2021 (2020). https://www.mdpi.com/2076-3417/10/6/2021
Wang, X., Lu, Y., Wang, Y., Chen, W.B.: Diabetic retinopathy stage classification using convolutional neural networks. In: 2018 IEEE International Conference on Information Reuse and Integration (IRI), pp. 465–471. IEEE (2018)
Lam, C., Yi, D., Guo, M., Lindsey, T.: Automated detection of diabetic retinopathy using deep learning. AMIA Summits Transl. Sci. Proc. 2018, 147–155 (2018)
Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., Wang, T.: Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–11 (2017)
Mohammadian, S., Karsaz, A., Roshan, Y.M.: Comparative study of fine-tuning of pre-trained convolutional neural networks for diabetic retinopathy screening. In: 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), pp. 1–6 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Laribi, N. et al. (2023). A Progressive Deep Transfer Learning for the Diagnosis of Alzheimer’s Disease on Brain MRI Images. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-28540-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28539-4
Online ISBN: 978-3-031-28540-0
eBook Packages: Computer ScienceComputer Science (R0)