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Alzheimer Disease Detection Using MRI: Deep Learning Review

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A Correction to this article was published on 04 June 2024

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Abstract

Deep learning for Alzheimer disease detection using MRI is an emerging area of research in medical image processing. With the advent of new technologies based on methods of Deep Learning, medical diagnosis of certain diseases has become possible. Alzheimer’s is a disease which till date has no cure but the progression of the disease can be slowed down or a person who might develop Alzheimer in future can be treated if early prediction of it is possible. Early prediction of the disease benefits medical professionals a lot for the correct diagnosis. Medical professionals label Alzheimer patients based on the progression of the disease as AD (Alzheimer’s), CN (cognitive impairment) and MCI (mild cognitive impairment). In literature many Deep Learning models are used for the early detection of Alzheimer’s disease. Though there are many image modalities, MRI images being non-invasive are considered best for these types of medical experiments. In the present study, we have studied the evolution of Alzheimer’s disease over time, research gaps, challenges towards building advanced models, possible recommendations to overcome those challenges and determining the best performance model. We have focussed on an exhaustive and comprehensive survey of very deep learning-based research papers on Alzheimer’s disease detection. The present work will benefit researchers by providing a clear direction for future scope in Alzheimer disease detection and analysis.

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Data availability

The data that support the findings of this study are openly available in

1. Alzheimer's Disease Neuroimaging Initiative (ADNI) at https://adni.loni.usc.edu/about/.

2. Open Access Series of Imaging Studies (OASIS) at https://sites.wustl.edu/oasisbrains/.

3. Alzheimer's Dataset ( 4 class of Images) at https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images.

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References

  1. Yetman D. Dementia and Alzheimer’s: what are the differences? 2021. https://www.healthline.com/health/alzheimers-disease/difference-dementia-alzheimers.

  2. Leonard W. Symptoms of Dementia. 2018. https://www.healthline.com/health/dementia-symptoms#takeaway.

  3. Dementia. https://my.clevelandclinic.org/health/diseases/9170-dementia.

  4. Overview—Alzheimer’s disease. https://www.nhs.uk/conditions/alzheimers-disease/.

  5. Gaugler J, James B, Johnson T, Reimer J, Solis M, Weuve J, Hohman TJ. 2022 Alzheimer’s disease facts and figures. Alzheimers & Dementia. 2022;18(4):700–89.

    Article  Google Scholar 

  6. Rajan KB, Weuve J, Barnes LL, McAninch EA, Wilson RS, Evans DA. Population estimate of people with clinical AD and mild cognitive impairment in the United States (2020–2060). Alzheimers Dementia. 2021. https://doi.org/10.1002/alz.12362.

    Article  Google Scholar 

  7. Shaji KS, Jotheeswaran AT, Girish N. Alzheimer’s & related disorders Society of India. The dementia India report—prevalence, impact, costs and services for Dementia: Executive summary. 2010.

  8. Alzheimer’s and normal brains, comparison [Photograph]. https://www.sciencephoto.com/media/1137925/view/alzheimer-s-and-normal-brains-comparison.

  9. ADNI Alzheimer’s disease neuroimaging initiative. https://adni.loni.usc.edu/about/.

  10. OASIS open access series of imaging studies. https://www.oasis-brains.org/.

  11. Cagler U. What is Kaggle? 2022. https://www.datacamp.com/blog/what-is-kaggle.

  12. [ThePIRL]. How MRI works: Part 1 NMR Basics [Video]. https://www.youtube.com/watch?v=TQegSF4ZiIQ&t=262s

  13. Preston DC. Magnetic resonance imaging (mri) of the brain and spine: basics. MRI Basics, Case Med. 2006;30:1–6.

    Google Scholar 

  14. Hirsch GV, Bauer CM, Merabet LB. Using structural and functional brain imaging to uncover how the brain adapts to blindness. Ann Neurosci Psychol. 2015;2:7.

    Google Scholar 

  15. Mukhtar G, Farhan S. Convolutional neural network-based prediction of conversion from mild cognitive impairment to Alzheimer’s disease: a technique using hippocampus extracted from MRI. Adv Electr Comput Eng. 2020;20(2):113–22.

    Article  Google Scholar 

  16. Li F, Tran L, Thung KH, Ji S, Shen D, Li J. A robust deep model for improved classification of AD/MCI patients. IEEE J Biomed Health Inform. 2015;19(5):1610–6.

    Article  Google Scholar 

  17. Sarraf S, Tofighi G. Classification of Alzheimer’s disease using FMRI data and deep learning convolutional neural networks. arXiv preprint, 2016. https://arXiv.org/1603.08631.

  18. Hon M, Khan NM. Towards Alzheimer’s disease classification through transfer learning. In: 2017 IEEE International conference on bioinformatics and biomedicine (BIBM). IEEE. 2017, pp 1166–1169.

  19. Gunawardena KANNP, Rajapakse RN, Kodikara ND. Applying convolutional neural networks for pre-detection of Alzheimer’s disease from structural MRI data. In: 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, 2017, pp. 1–7.

  20. Basaia S, Agosta F, Wagner L, Canu E, Magnani G, Santangelo R, Alzheimer’s Disease Neuroimaging Initiative. Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clin. 2019;21:101645.

    Article  Google Scholar 

  21. Hussain E, Hasan M, Hassan SZ, Azmi TH, Rahman MA, Parvez MZ. Deep learning based binary classification for Alzheimer’s disease detection using brain MRI images. In: 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE. 2020, pp. 1115–1120.

  22. Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, Yaqub M. A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI images. Neuroscience. 2021;460:43–52.

    Article  Google Scholar 

  23. Al-Khuzaie FE, Bayat O, Duru AD. Diagnosis of Alzheimer disease using 2D MRI slices by convolutional neural network. Appl Bionics Biomech. 2021;2021:1–9.

    Article  Google Scholar 

  24. Arafa DA, Moustafa HED, Ali HA, Ali-Eldin AM, Saraya SF. A deep learning framework for early diagnosis of Alzheimer’s disease on MRI images. Multimed Tools Appl. 2023;83:1–33.

    Google Scholar 

  25. Jain R, Jain N, Aggarwal A, Hemanth DJ. Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cogn Syst Res. 2019;57:147–59.

    Article  Google Scholar 

  26. Nawaz A, Anwar SM, Liaqat R, Iqbal J, Bagci U, Majid M. Deep convolutional neural network based classification of Alzheimer’s disease using MRI data. In: 2020 IEEE 23rd International Multitopic Conference (INMIC). IEEE, 2020, pp. 1–6.

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

    Google Scholar 

  28. Yildirim M, Cinar A. Classification of Alzheimer’s disease MRI images with CNN based hybrid method. Ing Syst Inf. 2020;25(4):413–8.

    Google Scholar 

  29. Fuadah YN, Wijayanto I, Pratiwi NKC, Taliningsih FF, Rizal S, Pramudito MA. Automated classification of Alzheimer’s disease based on MRI image processing using convolutional neural network (CNN) with AlexNet architecture. J Phys Conf Ser. 2021;1844(1):012020.

    Article  Google Scholar 

  30. Buvaneswari PR, Gayathri R. Deep learning-based segmentation in classification of Alzheimer’s disease. Arab J Sci Eng. 2021;46:5373–83.

    Article  Google Scholar 

  31. Prajapati R, Khatri U, Kwon GR. An efficient deep neural network binary classifier for Alzheimer’s disease classification. In: 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2021, pp. 231–234.

  32. Almadhoun HR, Abu-Naser SS. Classification of Alzheimer’s disease using traditional classifiers with pre-trained CNN. Int J Acad Health Med Res (IJAHMR). 2021;5(4):17–21.

    Google Scholar 

  33. Pradhan A, Gige J, Eliazer M. Detection of Alzheimer’s disease (AD) in MRI images using deep learning. Int J Eng Res Technol (IJERT). 2021;10:580–5.

    Google Scholar 

  34. Ajagbe SA, Amuda KA, Oladipupo MA, Afe OF, Okesola KI. Multi-classification of Alzheimer disease on magnetic resonance images (MRI) using deep convolutional neural network (DCNN) approaches. Int J Adv Comput Res. 2021;11:53.

    Article  Google Scholar 

  35. Murugan S, Venkatesan C, Sumithra MG, Gao XZ, Elakkiya B, Akila M, Manoharan S. DEMNET: a deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images. IEEE Access. 2021;9:9.

    Article  Google Scholar 

  36. Hazarika RA, Kandar D, Maji AK. An experimental analysis of different deep learning based models for Alzheimer’s disease classification using brain magnetic resonance images. J King Saud Univ-Comput Inform Sci. 2022;34(10):8576–98.

    Google Scholar 

  37. Sharma S, Guleria K, Tiwari S, Kumar S. A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans. Meas Sens. 2022;24:100506.

    Article  Google Scholar 

  38. Samhan LF, Alfarra AH, Abu-Naser SS. Classification of Alzheimer’s disease using convolutional neural networks. Int J Acad Inform Syst Res (IJAISR). 2022;6(3):18–23.

    Google Scholar 

  39. Shanmugam JV, Duraisamy B, Simon BC, Bhaskaran P. Alzheimer’s disease classification using pre-trained deep networks. Biomed Signal Process Control. 2022;71: 103217.

    Article  Google Scholar 

  40. Marwa EG, Moustafa HED, Khalifa F, Khater H, AbdElhalim E. An MRI-based deep learning approach for accurate detection of Alzheimer’s disease. Alex Eng J. 2023;63:211–21.

    Article  Google Scholar 

  41. Bhagat A, Ansarullah SI, Othman MTB, Hamid Y, Alkahtani HK, Ullah I, Hamam H. A novel framework for classification of different Alzheimer’s disease stages using CNN model. Electronics. 2023;12(2):469.

    Article  Google Scholar 

  42. Raza N, Naseer A, Tamoor M, Zafar K. Alzheimer disease classification through transfer learning approach. Diagnostics. 2023;13(4):801.

    Article  Google Scholar 

  43. El-Latif AAA, Chelloug SA, Alabdulhafith M, Hammad M. Accurate detection of Alzheimer’s disease using lightweight deep learning model on MRI data. Diagnostics. 2023;13(7):1216.

    Article  Google Scholar 

  44. McKenna MC, Dienel GA, Sonnewald U, Waagepetersen HS, Schousboe A. Energy metabolism of the brain. In: Basic neurochemistry. Academic Press; 2012. p. 200–31.

    Chapter  Google Scholar 

  45. Tashiro A, Aaron G, Aronov D, Cossart R, Dumitriu D, Fenstermaker V, Yuste R. Imaging brain slices. In: Handbook of biological confocal microscopy. Springer; 2006. p. 722–35.

    Chapter  Google Scholar 

  46. Leung LW, King B, Vohora V. Comparison of image data fusion techniques using entropy and INI. In: 22nd Asian Conference on Remote Sensing 2001; vol 5, no. 9, pp. 152–157.

  47. Mello Román JC, Vázquez Noguera JL, Legal-Ayala H, Pinto-Roa DP, Gomez-Guerrero S, García Torres M. Entropy and contrast enhancement of infrared thermal images using the multiscale top-hat transform. Entropy. 2019;21(3):244.

    Article  MathSciNet  Google Scholar 

  48. Fawzi A, Achuthan A, Belaton B. Brain image segmentation in recent years: a narrative review. Brain Sci. 2021;11(8):1055.

    Article  Google Scholar 

  49. Das D, Kalita SK. Skull stripping of brain MRI for analysis of Alzheimer’s disease. Int J Biomed Eng Technol. 2021;36(4):331–49.

    Article  Google Scholar 

  50. Kalavathi P, Prasath VB. Methods on skull stripping of MRI head scan images—a review. J Digit Imaging. 2016;29(3):365–79.

    Article  Google Scholar 

  51. Rosebrock A. Image classification basics. 2021 https://Pyimagesearch.com/2021/04/17/Image-Classification-Basics/.

  52. Watts R. Transverse view of the brain. 2007. https://static.sciencelearn.org.nz/images/images/000/001/104/embed/75yo_male.jpg?1674165508.

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Authors and Affiliations

  1. Department of Computer Science, Gauhati University, Guwahati, India

    Pallavi Saikia & Sanjib Kumar Kalita

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  1. Pallavi Saikia
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Correspondence to Pallavi Saikia.

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Saikia, P., Kalita, S.K. Alzheimer Disease Detection Using MRI: Deep Learning Review. SN COMPUT. SCI. 5, 507 (2024). https://doi.org/10.1007/s42979-024-02868-4

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