Skip to main content

Advertisement

SpringerLink
Log in

Deep learning-based approach for multi-stage diagnosis of Alzheimer’s disease

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Alzheimer’s Disease (AD) is a common neurological brain disorder that causes the brain cells to die and shrink (Atrophy) gradually, resulting in a continuous decline in one’s ability to function independently. Early diagnosis increases the possibility of preventing or delaying the advancement of this mental disorder. Magnetic Resonance Imaging (MRI) offers the potential of non-invasive longitudinal monitoring and plays a vital role as a biomarker of the disease progression. Structural Magnetic Resonance Imaging (sMRI) helps to measure Atrophy, which is considered to be the most dependable biomarker to assess the exact stage and severity of the neuro-degenerative aspect of AD pathology. There are five stages associated with AD, which include Normal Control (NC), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer’s Disease (AD). In this work, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI2) sMRI image dataset to measure and classify the stage of AD. In recent years, Convolutional Neural Networks (CNNs) are widely used for medical image analysis. This work focuses on applying different Deep Learning algorithms for the multi-class classification of AD MRI images and proposes the best pre-trained model that can accurately predict the patient’s stage. It is observed that ResNet-50v2 gives the best accuracy of 91.84% and f1-score of 0.97 for AD class. Visualization techniques such as Grad-CAM and Saliency Map are applied on the model that gave the best accuracy to understand the region of focus in the image which led to predicting its class.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the first author upon reasonable request.

Code availability

The code is available from the first author upon reasonable request.

References

  1. Aloysius N, Geetha M (2017) A review on deep convolutional neural networks. In: 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, pp 0588–0592

  2. Al-Shoukry S, Rassem TH, Makbol NM (2020) Alzheimer’s diseases detection by using deep learning algorithms: a mini-review. IEEE Access 8:77131–77141

    Article  Google Scholar 

  3. Altinkaya E, Polat K, Barakli B (2020) Detection of Alzheimer’s disease and dementia states based on deep learning from MRI images: a comprehensive review. J Electr Comput Eng 1(1):39–53

    Google Scholar 

  4. Alzheimer ADNI dataset. (2004). [Online]. https://adni.loni.usc.edu/data-samples/access-data/

  5. Bae JB, Lee S, Jung W, Park S, Kim W, Oh H, Han JW, Kim GE, Kim JS, Kim JH et al (2020) Identification of Alzheimer’s disease using a convolutional neural network model based on t1-weighted magnetic resonance imaging. Sci Rep 10(1):1–10

    Article  Google Scholar 

  6. Basaia S, Agosta F, Wagner L, Canu E, Magnani G, Santangelo R, Filippi M, Alzheimer’s Disease Neuroimaging Initiative et al (2019) Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clinical 21:101645

  7. Burgos N, Bottani S, Faouzi J, Thibeau-Sutre E, Colliot O (2021) Deep learning for brain disorders: from data processing to disease treatment. Brief Bioinform 22(2):1560–1576

    Article  Google Scholar 

  8. Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, Vitali P, Anzalone N, Bernini S, Palesi F et al (2020) A machine learning approach for the differential diagnosis of Alzheimer and vascular dementia fed by MRI selected features. Front Neuroinform 14:25

    Article  Google Scholar 

  9. Cedazo-Minguez A, Winblad B (2010) Biomarkers for Alzheimer’s disease and other forms of dementia: clinical needs, limitations and future aspects. Exp Gerontol 45(1):5–14

    Article  Google Scholar 

  10. Chandra A, Dervenoulas G, Politis M (2019) Magnetic resonance imaging in Alzheimer’s disease and mild cognitive impairment. J Neurol 266(6):1293–1302

    Article  Google Scholar 

  11. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Article  Google Scholar 

  12. Chitradevi D, Prabha S (2020) Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease. Appl Soft Comput 86:105857

    Article  Google Scholar 

  13. Cohen DS, Carpenter KA, Jarrell JT, Huang X, Initiative ADN et al (2019) Deep learning-based classification of multi-categorical Alzheimer’s disease data. Curr Neurobiol 10(3):141

    Google Scholar 

  14. Farooq A, Anwar S, Awais M, Rehman S (2017) A deep CNN based multi-class classification of Alzheimer’s disease using MRI. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, pp 1–6

  15. Feng W, Halm-Lutterodt NV, Tang H, Mecum A, Mesregah MK, Ma Y, Li H, Zhang F, Wu Z, Yao E et al (2020) Automated MRI-based deep learning model for detection of Alzheimer’s disease process. Int J Neural Syst 30(06):2050032

    Article  Google Scholar 

  16. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 770–778

  17. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 4700–4708

  18. Islam J, Zhang Y (2018) Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inform 5(2):1–14

    Article  Google Scholar 

  19. Islam J, Zhang Y (2017) A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. In: International conference on brain informatics. Springer, pp 213–222

  20. Jo T, Nho K, Saykin AJ (2019) Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 11:220

    Article  Google Scholar 

  21. Khan R, Akbar S, Mehmood A, Shahid F, Munir K, Ilyas N, Asif M, Zheng Z (2022) A transfer learning approach for multiclass classification of Alzheimer’s disease using MRI images. Front Neurosci 16

  22. Knight M, McCann B, Kauppinen R, Coulthard E (2016) Magnetic resonance imaging to detect early molecular and cellular changes in Alzheimer’s disease. Front Sging Neurosci 8:139. https://doi.org/10.3389/fnagi.2016.00139

    Article  Google Scholar 

  23. Lee G, Nho K, Kang B, Sohn K-A, Kim D (2019) Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Sci Rep 9(1):1–12

    Google Scholar 

  24. Lin W, Tong T, Gao Q, Guo D, Du X, Yang Y, Guo G, Xiao M, Du M, Qu X et al (2018) Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment. Front Neurosci 12:777

    Article  Google Scholar 

  25. Lu X, Wu H, Zeng Y (2019) Classification of Alzheimer’s disease in mobilenet. In: Journal of Physics: Conference Series, vol 1345, no 4. IOP Publishing, p 042012

  26. Ma D, Lu D, Popuri K, Wang L, Beg MF, Initiative ADN et al (2020) Differential diagnosis of frontotemporal dementia, Alzheimer’s disease, and normal aging using a multi-scale multi-type feature generative adversarial deep neural network on structural magnetic resonance images. Front Neurosci 14:853

    Article  Google Scholar 

  27. Magnin B, Mesrob L, Kinkingnéhun S, Pélégrini-Issac M, Colliot O, Sarazin M, Dubois B, Lehéricy S, Benali H (2009) Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51(2):73–83

    Article  Google Scholar 

  28. McCrackin L (2018) Early detection of Alzheimer’s disease using deep learning. In: Canadian Conference on Artificial Intelligence. Springer, pp 355–359

  29. Murugan S, Venkatesan C, Sumithra M, Gao X-Z, Elakkiya B, Akila M, Manoharan S (2021) DemNet: a deep learning model for early diagnosis of Alzheimer diseases and dementia from mr images. IEEE Access 9:90319–90329

    Article  Google Scholar 

  30. Nagaraj S, Duong TQ (2021) Deep learning and risk score classification of mild cognitive impairment and Alzheimer’s disease. J Alzheimers Dis (Preprint):1–12

  31. Nair JJ, Mohan N (2017) Alzheimer’s disease diagnosis in MR images using statistical methods. In: 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, pp 1232–1235

  32. Noor MBT, Zenia NZ, Kaiser MS, Al Mamun S, Mahmud M (2020) Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease. Parkinson’s disease and schizophrenia. Brain Inform 7(1):1–21

    Google Scholar 

  33. Odusami M, Maskeliūnas R, Damaševičius R, Krilavičius T (2021) Analysis of features of Alzheimer’s disease: detection of early stage from functional brain changes in magnetic resonance images using a finetuned resnet18 network. Diagnostics 11(6):1071

    Article  Google Scholar 

  34. Oh K, Chung Y-C, Kim KW, Kim W-S, Oh I-S (2019) Classification and visualization of Alzheimer’s disease using volumetric convolutional neural network and transfer learning. Sci Rep 9(1):1–16

    Article  Google Scholar 

  35. Pereira MEDC et al (2019) An extended-2D CNN approach for diagnosis of Alzheimer’s disease through structural MRI: Abordagem CNN 2D estendida para o diagnóstico da doença de alzheimer através de imagens de ressonância magnética estrutural

  36. Rana SS, Ma X, Pang W, Wolverson E (2020) A multi-modal deep learning approach to the early prediction of mild cognitive impairment conversion to Alzheimer’s disease. In: 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). IEEE, pp 9–18

  37. Rasmussen J, Langerman H (2019) Alzheimer’s disease-why we need early diagnosis. Degener Neurol Neuromuscul Dis 9:123

    Google Scholar 

  38. Sarraf S, Tofighi G (2016) Classification of Alzheimer’s disease structural MRI data by deep learning convolutional neural networks. Preprint at http://arxiv.org/abs/1607.06583

  39. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization, in Proceedings of the IEEE International Conference on Computer Vision. pp 618–626

  40. Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

    Article  Google Scholar 

  41. Spasov SE, Passamonti L, Duggento A, Liò P, Toschi N (2018) 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). pp 1271–1274

  42. Thushara A, Amma CU, John A, Saju R (2020) Multimodal MRI based classification and prediction of Alzheimer’s disease using random forest ensemble. In: 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA). IEEE, pp. 249–256

  43. Toshkhujaev S, Lee KH, Choi KY, Lee JJ, Kwon G-R, Gupta Y, Lama RK (2020) Classification of Alzheimer’s disease and mild cognitive impairment based on cortical and subcortical features from MRI t1 brain images utilizing four different types of datasets, Journal of Healthcare Engineering, vol. 2020

  44. Trojachanec K, Kitanovski I, Dimitrovski I, Loshkovska S (2017) Longitudinal brain MRI retrieval for Alzheimer’s disease using different temporal information. IEEE Access 6:9703–9712

    Article  Google Scholar 

  45. Tufail AB, Ma Y-K, Zhang Q-N (2020) Binary classification of Alzheimer’s disease using sMRI imaging modality and deep learning. J Digit Imaging 33(5):1073–1090

    Article  Google Scholar 

  46. Van der Maaten L, Hinton G (2008) Visualizing data using T-SNE. J Mach Learn Res 9(11)

  47. Veetil IK, Gopalakrishnan E, Sowmya V, Soman K (2021) Parkinson’s disease classification from magnetic resonance images (MRI) using deep transfer learned convolutional neural networks. In: 2021 IEEE 18th India Council International Conference (INDICON). IEEE, pp 1–6

  48. Vemuri P, Jack CR (2010) Role of structural MRI in Alzheimer’s disease. Alzheimers Res Ther 2(4):1–10

    Article  Google Scholar 

  49. Venugopalan J, Tong L, Hassanzadeh HR, Wang MD (2021) Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci Rep 11(1):1–13

    Article  Google Scholar 

  50. Wada A, Tsuruta K, Irie R, Kamagata K, Maekawa T, Fujita S, Koshino S, Kumamaru K, Suzuki M, Nakanishi A et al (2019) Differentiating Alzheimer’s disease from dementia with lewy bodies using a deep learning technique based on structural brain connectivity. Magn Reson Med Sci 18(3):219

    Article  Google Scholar 

  51. Xu Z, Shen X, Pan W, Initiative ADN (2014) Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes. PLoS ONE 9(8):e102312

    Article  Google Scholar 

  52. Zhang T, Zhao Z, Zhang C, Zhang J, Jin Z, Li L (2019) Classification of early and late mild cognitive impairment using functional brain network of resting-state fMRI. Front Psych 10:572

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Computational Engineering and Networking (CEN), Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Srividhya L, Sowmya V, Gopalakrishnan E.A & Soman K.P

  2. Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Vinayakumar Ravi

Authors
  1. Srividhya L
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sowmya V
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinayakumar Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gopalakrishnan E.A
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Soman K.P
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vinayakumar Ravi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

L, S., V, S., Ravi, V. et al. Deep learning-based approach for multi-stage diagnosis of Alzheimer’s disease. Multimed Tools Appl 83, 16799–16822 (2024). https://doi.org/10.1007/s11042-023-16026-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16026-0

Keywords

Search

Navigation