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A CNN based framework for classification of Alzheimer’s disease

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In the current decade, advances in health care are attracting widespread interest due to their contributions to people longer surviving and fitter lives. Alzheimer’s disease (AD) is the commonest neurodegenerative and dementing disease. The monetary value of caring for Alzheimer’s disease patients is involved to rise dramatically. The necessity of having a computer-aided system for early and accurate AD classification becomes crucial. Deep-learning algorithms have notable advantages rather than machine learning methods. Many recent research studies that have used brain MRI scans and convolutional neural networks (CNN) achieved promising results for the diagnosis of Alzheimer’s disease. Accordingly, this study proposes a CNN based end-to-end framework for AD-classification. The proposed framework achieved 99.6%, 99.8%, and 97.8% classification accuracies on Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset for the binary classification of AD and Cognitively Normal (CN). In multi-classification experiments, the proposed framework achieved 97.5% classification accuracy on the ADNI dataset.

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  1. A complete listing of ADNI investigators can be found at: ”


  1. Aderghal K, Khvostikov A, Krylov A, Benois-Pineau J, Afdel K, Catheline G (2018) 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.

  2. Altaf T, Anwar SM, Gul N, Majeed MN, Majid M (2018) Multi-class alzheimer’s disease classification using image and clinical features. Biomed Signal Process Control 43:64–74

    Article  Google Scholar 

  3. Asim Y, Raza B, Malik AK, Rathore S, Hussain L, Iftikhar MA (2018) A multi-modal, multi-atlas-based approach for alzheimer detection via machine learning. Int J Imaging Syst Technol 28(2):113–123

    Article  Google Scholar 

  4. Baldacci F, Lista S, O’Bryant SE, Ceravolo R, Toschi N, Hampel H, Initiative APM et al (2018) Blood-based biomarker screening with agnostic biological definitions for an accurate diagnosis within the dimensional spectrum of neurodegenerative diseases. In: Biomarkers for alzheimer’s disease drug development. Springer, pp 139–155

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

    Article  Google Scholar 

  6. Basheera S (2020) A novel CNN based alzheimer’s disease classification using hybrid enhanced ICA segmented gray matter of MRI. Comput Med Imaging Graph 18:101713

    Article  Google Scholar 

  7. Beheshti I, Demirel H, Matsuda H, Initiative ADN et al (2017) Classification of alzheimer’s disease and prediction of mild cognitive impairment-to-alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 83:109–119

    Article  Google Scholar 

  8. Beheshti I, Maikusa N, Matsuda H, Demirel H, Anbarjafari G (2017) Histogram-based feature extraction from individual gray matter similarity-matrix for alzheimer’s disease classification. J Alzheimers Dis 55(4):1571–1582

    Article  Google Scholar 

  9. Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of (COMPSTAT’2010). Springer, pp 177–186

  10. Braak H, Braak E (1995) Staging of alzheimer’s disease-related neurofibrillary changes. Neurobiol Aging 16(3):271–278

    Article  MathSciNet  Google Scholar 

  11. Bruscoli M, Lovestone S (2004) Is MCI really just early dementia? A systematic review of conversion studies. Int Psychogeriatr 16(2):129–140

    Article  Google Scholar 

  12. Choi JY, Lee B (2020) Combining of multiple deep networks via ensemble generalization loss, based on MRI images, for alzheimer’s disease classification. IEEE Signal Process Lett 27:206–210

    Article  Google Scholar 

  13. Delacourte A, David JP, Sergeant N, Buee L, Wattez A, Vermersch P, Ghozali F, Fallet-Bianco C, Pasquier F, Lebert F et al (1999) The biochemical pathway of neurofibrillary degeneration in aging and alzheimer’s disease. Neurology 52(6):1158–1158

    Article  Google Scholar 

  14. Duraisamy B, Shanmugam JV, Annamalai J (2018) Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network. Brain Imaging Behav 13:1–24

    Google Scholar 

  15. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

  16. Golik P, Doetsch P, Ney H (2013) Cross-entropy vs. squared error training: a theoretical and experimental comparison. In: Interspeech, vol 13, pp 1756–1760

  17. Hampel H, Toschi N, Baldacci F, Zetterberg H, Blennow K, Kilimann I, Teipel SJ, Cavedo E, Melo dos Santos A, Epelbaum S et al (2018) Alzheimer’s disease biomarker-guided diagnostic workflow using the added value of six combined cerebrospinal fluid candidates: Aβ1-42, total-tau, phosphorylated-tau, nfl, neurogranin, and ykl-40. Alzheimer’s & Dementia 14(4):492–501

    Article  Google Scholar 

  18. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167

  19. Jain R, Jain N, Aggarwal A, Hemanth DJ (2019) Convolutional neural network based alzheimer’s disease classification from magnetic resonance brain images. Cogn Syst Res 57:147–159

    Article  Google Scholar 

  20. Janocha K, Czarnecki WM (2017) On loss functions for deep neural networks in classification. arXiv:1702.05659

  21. 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 

  22. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization.

  23. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  24. Lee B, Ellahi W, Choi JY (2019) Using deep CNN with data permutation scheme for classification of alzheimer’s disease in structural magnetic resonance imaging (SMRI). IEICE Trans Inf Syst 102(7):1384–1395

    Article  Google Scholar 

  25. Liu M, Cheng D, Wang K, Wang Y (2018) Multi-modality cascaded convolutional neural networks for alzheimer’s disease diagnosis. Neuroinformatics 16(3–4):295–308.

    Article  Google Scholar 

  26. Markesbery WR (2010) Neuropathologic alterations in mild cognitive impairment: a review. J Alzheimers Dis 19(1):221–228

    Article  Google Scholar 

  27. Nakerst G, Brennan J, Haque M (2020) Gradient descent with momentum—to accelerate or to super-accelerate? arXiv:2001.06472

  28. Nwankpa CE, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning. arXiv:1811.03378

  29. Payan A, Montana G (2015) Predicting alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv:1502.02506

  30. Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229

    Article  Google Scholar 

  31. Prince M, Wimo A, Guerchet M, Ali G, Wu Y, Prina M et al (2019) Alzheimer’s disease international: World alzheimer report 2015: the global impact of dementia: an analysis of prevalence, incidence, cost and trends 2015. Alzheimer’s Disease International, London

    Google Scholar 

  32. Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C (2017) A review on neuroimaging-based classification studies and associated feature extraction methods for alzheimer’s disease and its prodromal stages. NeuroImage 155:530–548

    Article  Google Scholar 

  33. Rizzi L, Rosset I, Roriz-Cruz M (2014) Global epidemiology of dementia: Alzheimer’s and vascular types. BioMed research international

  34. Samper-Gonzalez J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J et al (2018) Reproducible evaluation of classification methods in alzheimer’s disease: framework and application to MRI and pet data. NeuroImage 183:504–521

    Article  Google Scholar 

  35. Sarraf S, Tofighi G (2016) Deep learning-based pipeline to recognize alzheimer’s disease using FMRI data. In: Future technologies conference (FTC). IEEE, pp 816–820

  36. Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT (2011) Neuropathological alterations in alzheimer disease. Cold Spring Harbor Perspect Med 1(1):a006189

    Article  Google Scholar 

  37. Sørensen L, Igel C, Pai A, Balas I, Anker C, Lillholm M, Nielsen M, Initiative ADN et al (2017) Differential diagnosis of mild cognitive impairment and alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. Neuro Image Clin 13:470–482

    Google Scholar 

  38. Teipel SJ, Cavedo E, Lista S, Habert MO, Potier MC, Grothe MJ, Epelbaum S, Sambati L, Gagliardi G, Toschi N et al (2018) Effect of alzheimer’s disease risk and protective factors on cognitive trajectories in subjective memory complainers: an insight-pread study. Alzheimer’s & Dementia 14(9):1126–1136

    Article  Google Scholar 

  39. Vieira S, Pinaya WH, Mechelli A (2017) Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci Biobehav Rev 74:58–75

    Article  Google Scholar 

  40. Wang S, Wang H, Cheung AC, Shen Y, Gan M (2020) Ensemble of 3d densely connected convolutional network for diagnosis of mild cognitive impairment and alzheimer’s disease. In: Deep learning applications. Springer, pp 53–73

  41. Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42(5):85

    Article  Google Scholar 

  42. Wimo A, Guerchet M, Ali GC, Wu YT, Prina AM, Winblad B, Jönsson L, Liu Z, Prince M (2017) The worldwide costs of dementia 2015 and comparisons with 2010. Alzheimer’s & Dementia 13(1):1–7

    Article  Google Scholar 

  43. Zhang Y, Wang S, Sui Y, Yang M, Liu B, Cheng H, Sun J, Jia W, Phillips P, Gorriz JM (2018) Multivariate approach for alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J Alzheimers Dis 65(3):855–869

    Article  Google Scholar 

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Data used in the preparation of this article were obtained from the Alzheimers disease Database Initiative (ADNI) database. A complete listing of ADNI investigators can be found at:

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Correspondence to Yousry AbdulAzeem.

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Yousry AbdulAzeem, Waleed Bahgat, and Mahmoud Badawy declare that they have no conflict of interest.

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AbdulAzeem, Y., Bahgat, W.M. & Badawy, M. A CNN based framework for classification of Alzheimer’s disease. Neural Comput & Applic 33, 10415–10428 (2021).

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