Abstract
Deep Learning has shown promising results on the field of Alzheimer’s computerized diagnosis based on the neuroimaging data. Alzheimer disease is an irreversible and progressive neurodegenerative disorder that destroys gradually the brain cells. This chronic disease affect the ability of the person to carry out daily tasks. It caused many problems such as cognitive deficits, problem with recognition, memory loss and difficulties with thinking. The major breakthrough in neuroscience today is the accurate early detection of the Alzheimer’s disease based on various brain biomarkers. Magnetic resonance imaging (MRI) is a noninvasive brain modality widely used for brain diseases detection specifically Alzheimer disease. It visualize a discriminate feature of the neurodegeneration which is the progressive cerebral atrophy. Various studies based on deep learning models have been proposed for the task of Alzheimer’s disease classification and prediction from brain MRI scans. However these models have been implemented from scratch. The training from scratch is tedious and time-consuming task. In this paper we conduct an analysis using Open Access Series of Imaging Studies (OASIS) dataset based on tuning different convolution neural networks. Further we propose a uni-modal Alzheimer method prediction using Efficientnet network. The Efficientnet network solve the main issues of the existing convolution neural network. Data preparation includes some pre-processing steps such as image resizing. We achieve 79% using VGG16, 92% using Resenet,93% using Densenet and we produce an accuracy of 96% using the Efficientnet 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
Similar content being viewed by others
References
Abrol, A., Fu, Z., Du, Y., Calhoun, V.D.: Multimodal data fusion of deep learning and dynamic functional connectivity features to predict Alzheimer’s disease progression. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE (July 2019). https://doi.org/10.1109/embc.2019.8856500
Aderghal, K., Khvostikov, A., Krylov, A., Benois-Pineau, J., Afdel, K., Catheline, G.: Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning. In: 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), IEEE (June 2018). https://doi.org/10.1109/cbms.2018.00067
Al-Khuzaie, F.E.K., Bayat, O., Duru, A.D.: Diagnosis of Alzheimer disease using 2d MRI slices by convolutional neural network. Appl. Bionics Biomech. 2021, 1–9 (2021). https://doi.org/10.1155/2021/6690539
Ashraf, A., Naz, S., Shirazi, S.H., Razzak, I., Parsad, M.: Deep transfer learning for alzheimer neurological disorder detection. Multimedia Tools Appl. 1–26 (2021). https://doi.org/10.1007/s11042-020-10331-8
Basheera, S., Ram, M.S.S.: Convolution neural network–based alzheimer’s disease classification using hybrid enhanced independent component analysis based segmented gray matter of t2 weighted magnetic resonance imaging with clinical valuation. Alzheimer’s Dementia: Transl. Res. Clin. Interventions 5(1), 974–986 (2019). https://doi.org/10.1016/j.trci.2019.10.001
de Carvalho Pereira, M.E., Fantini, I., Lotufo, R.A., Rittner, L.: An extended-2d CNN for multiclass alzheimer’s disease diagnosis through structural MRI. In: Hahn, H.K., Mazurowski, M.A. (eds.) Medical Imaging 2020: Computer-Aided Diagnosis. SPIE (March 2020). https://doi.org/10.1117/12.2550753
Gorji, K.: A deep learning approach for diagnosis of mild cognitive impairment based on MRI images. Brain Sci. 9(9), 217 (2019). https://doi.org/10.3390/brainsci9090217
Islam, J., Zhang, Y.: Early diagnosis of alzheimer’s disease: a neuroimaging study with deep learning architectures. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE (June 2018). https://doi.org/10.1109/cvprw.2018.00247
Jain, R., Jain, N., Aggarwal, A., Hemanth, D.J.: Convolutional neural network based alzheimer’s disease classification from magnetic resonance brain images. Cogn. Syst. Res. 57, 147–159 (2019). https://doi.org/10.1016/j.cogsys.2018.12.015
Lin, W., et al.: Convolutional neural networks-based MRI image analysis for the alzheimer’s disease prediction from mild cognitive impairment. Front. Neurosci. 12 (November 2018). https://doi.org/10.3389/fnins.2018.00777
Liu, M., et al.: A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in alzheimer’s disease. NeuroImage 208, (2020). https://doi.org/10.1016/j.neuroimage.2019.116459
Nanni, L., et al.: Comparison of transfer learning and conventional machine learning applied to structural brain MRI for the early diagnosis and prognosis of alzheimer’s disease. Front. Neurology 11 (November 2020). https://doi.org/10.3389/fneur.2020.576194
Nawaz, H., Maqsood, M., Afzal, S., Aadil, F., Mehmood, I., Rho, S.: A deep feature-based real-time system for Alzheimer disease stage detection. Multimedia Tools Appl. 1–19 (2020). https://doi.org/10.1007/s11042-020-09087-y
Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., Song, X.: Early detection of alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front. Neurosci. 14, (May 2020). https://doi.org/10.3389/fnins.2020.00259
Raju, M., Gopi, V.P., Anitha, V.S., Wahid, K.A.: Multi-class diagnosis of alzheimer’s disease using cascaded three dimensional-convolutional neural network. Phys. Eng. Sci. Med. 1–10 (2020). https://doi.org/10.1007/s13246-020-00924-w
Yagis, E., Citi, L., Diciotti, S., Marzi, C., Atnafu, S.W., Herrera, A.G.S.D.: 3d convolutional neural networks for diagnosis of alzheimer’s disease via structural MRI. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), IEEE (July 2020). https://doi.org/10.1109/cbms49503.2020.00020
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Kadri, R., Tmar, M., Bouaziz, B. (2021). Alzheimer’s Disease Prediction Using EfficientNet and Fastai. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-82147-0_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82146-3
Online ISBN: 978-3-030-82147-0
eBook Packages: Computer ScienceComputer Science (R0)