Abstract
Alzheimer’s disease (AD) is one of the most prevalent medical conditions with no effective medical treatment or cure. The issue lies in the fact that it is also a condition which is chronic, with irreversible effects on the brain, like cognitive impairment. The diagnosis of Alzheimer’s in elderly people is quite difficult and requires a highly discriminative feature representation for classification due to similar brain patterns and pixel intensities. Although we cannot prevent AD from developing, we can try to detect the stages of development of AD. In this paper, we explore and test the various methodologies used to classify Alzheimer’s Disease (AD), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), Mild Cognitive Impairment (MCI) and, healthy person (CN) using the Magnetic Resonance Image (MRI)s and Deep Learning techniques. The experiments are performed using ADNI dataset the results are obtained for multiple machine learning and deep learning methods that have been implemented over time. In our proposed work, we take into consideration the different stages of Dementia and Alzheimer’s Disease, and use Deep Learning models on the MRI scans for detecting and predicting which stage of Alzheimer’s or Dementia a person is suffering from.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Helaly, H.A., Badawy, M., Haikal, A.Y.: Deep learning approach for early detection of Alzheimer’s disease. Cogn. Comput. 14, 1711–1727 (2021)
Chandra, A., et al.: Magnetic resonance imaging in Alzheimer’s disease and mild cognitive impairment. J. Neurol. 266, 1293–1302 (2019)
Robinson, L., Tang, E., Taylor, J.-P.: Dementia: timely diagnosis and early intervention. Bmj 350 (2015)
Mittal, V.A., Walker, E.F.: Dyskinesias, tics, and psychosis: issues for the next diagnostic and statistical manuel of mental disorders. Psychiatry Res. 189(1), 158 (2011)
Hinrichs, C., et al.: Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 55(2), 574–589 (2011)
Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_72
Li, F., et al.: A robust deep model for improved classification of AD/MCI patients. IEEE J. Biomed. Health Inform. 19(5), 1610–1616 (2015)
Mirzaei, G., Adeli, H.: Machine learning techniques for diagnosis of Alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed. Signal Process. Control 72, 103293 (2022)
Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprint arXiv:1502.02506 (2015)
Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE (2016)
Sarraf, S., Tofighi, G.: Classification of Alzheimer’s disease structural MRI data by deep learning convolutional neural networks. arXiv preprint arXiv:1607.06583 (2016)
Wang, Y., et al.: A novel multimodal MRI analysis for Alzheimer’s disease based on convolutional neural network. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2018)
Bhatkoti, P., Paul, M.: Early diagnosis of Alzheimer’s disease: a multi-class deep learning framework with modified k-sparse autoencoder classification. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE (2016)
Gamal, A., Elattar, M., Selim, S.: Automatic early diagnosis of Alzheimer’s disease using 3D deep ensemble approach. IEEE Access 10, 115974–115987 (2022)
Liu, S., et al.: On the design of convolutional neural networks for automatic detection of Alzheimer’s disease. In: Machine Learning for Health Workshop. PMLR (2020)
Parmar, H., et al.: Spatiotemporal feature extraction and classification of Alzheimer’s disease using deep learning 3D-CNN for fMRI data. J. Med. Imaging 7(5), 056001–056001 (2020)
Kumar, L., Sathish, S., et al.: AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images. Mater. Today Proc. 51, 58–65 (2022)
Spasov, S.E., et al.: A multi-modal convolutional neural network framework for the prediction of Alzheimer’s disease. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2018)
Basaia, S., et al.: Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin. 21, 101645 (2019)
Pan, D., et al.: Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front. Neurosci. 14, 259 (2020)
Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Z. Med. Phys. 29(2), 102–127 (2019)
Gong, E., et al.: Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI. J. Magn. Reson. Imaging 48(2), 330–340 (2018)
Liu, F., et al.: Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology 286(2), 676–684 (2018)
Oakden-Rayner, L., et al.: Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework. Sci. Rep. 7(1), 1648 (2017)
LeCun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
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
Nair, N., Poornachandran, P., Sujadevi, V.G., Aravind, M. (2023). Alzheimer’s Detection and Prediction on MRI Scans: A Comparative Study. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-36402-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36401-3
Online ISBN: 978-3-031-36402-0
eBook Packages: Computer ScienceComputer Science (R0)