Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective

  • Manan Binth Taj Noor
  • Nusrat Zerin Zenia
  • M. Shamim KaiserEmail author
  • Mufti MahmudEmail author
  • Shamim Al Mamun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11976)


Rapid development of high speed computing devices and infrastructure along with improved understanding of deep machine learning techniques during the last decade have opened up possibilities for advanced analysis of neuroimaging data. Using those computing tools Neuroscientists now can identify Neurodegenerative diseases from neuroimaging data. Due to the similarities in disease phenotypes, accurate detection of such disorders from neuroimaging data is very challenging. In this article, we have reviewed the methodological research papers proposing to detect neurodegenerative diseases using deep machine learning techniques only from MRI data. The results show that deep learning based techniques can detect the level of disorder with relatively high accuracy. Towards the end, current challenges are reviewed and some possible future research directions are provided.


Machine learning Alzheimer’s disease Schizophrenia Parkinson’s disease MRI 


  1. 1.
    Amoroso, N., et al.: Deep learning reveals Alzheimer’s disease onset in MCI subjects: results from an international challenge. J. Neurosci. Methods 302, 3–9 (2018)CrossRefGoogle Scholar
  2. 2.
    Basaia, S., et al.: Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and DNN. Neuroimage Clin. 21, 101645 (2019)CrossRefGoogle Scholar
  3. 3.
    Bohle, M.: Layer-wise relevance propagation for explaining DNN decisions in MRI-based Alzheimer’s disease classification. Front. Aging Neurosci. 11, 194 (2019)CrossRefGoogle Scholar
  4. 4.
    Brown, C.J., Hamarneh, G.: Machine learning on human connectome data from MRI. CoRR abs/1611.08699 (2016)Google Scholar
  5. 5.
    Bäckström, K., et al.: An efficient 3D deep convolutional network for Alzheimer’s disease diagnosis using MR images. In: Proceedings of the ISBI 2018, pp. 149–153 (2018)Google Scholar
  6. 6.
    Dakka, J., et al.: Learning neural markers of schizophrenia disorder using recurrent neural networks. CoRR abs/1712.00512 (2017)Google Scholar
  7. 7.
    Dolph, C.V., Alam, M., Shboul, Z., Samad, M.D., Iftekharuddin, K.M.: Deep learning of texture and structural features for multiclass Alzheimer’s disease classification. In: Proceedings of the IJCNN 2017, pp. 2259–2266 (2017)Google Scholar
  8. 8.
    Esmaeilzadeh, S., Yang, Y., Adeli, E.: End-to-end Parkinson disease diagnosis using brain MR-images by 3D-CNN. CoRR abs/1806.05233 (2018)Google Scholar
  9. 9.
    Farooq, A., Anwar, S., Awais, M., Rehman, S.: A deep CNN based multi-class classification of Alzheimer’s disease using MRI. In: Proceedings of the IEEE IST 2017, pp. 1–6 (2017)Google Scholar
  10. 10.
    Gottapu, R.D., Dagli, C.H.: Analysis of Parkinson’s disease data. Proc. Comput. Sci. 140, 334–341 (2018)CrossRefGoogle Scholar
  11. 11.
    Han, S., et al.: Recognition of early-onset schizophrenia using deep-learning method. Appl. Inform. 4(1), 16 (2017)CrossRefGoogle Scholar
  12. 12.
    Islam, J., Zhang, Y.: A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. In: Zeng, Y., et al. (eds.) BI 2017. LNCS, vol. 10654, pp. 213–222. Springer, Cham (2017). Scholar
  13. 13.
    Kim, J., et al.: Deep NN with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. NeuroImage 124, 127–146 (2015)CrossRefGoogle Scholar
  14. 14.
    Kollia, I., Stafylopatis, A., Kollias, S.D.: Predicting Parkinson’s disease using latent information extracted from deep neural networks. CoRR abs/1901.07822 (2019)Google Scholar
  15. 15.
    Kollias, D., et al.: Deep neural architectures for prediction in healthcare. Complex Intell. Syst. 4(2), 119–131 (2018)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Latha, M., Kavitha, G.: Detection of Schizophrenia in brain MR images based on segmented ventricle region and DBNs. Neural Comput. Appl. 31, 5195–5206 (2018)CrossRefGoogle Scholar
  17. 17.
    Li, H., Fan, Y.: Early prediction of Alzheimer’s disease dementia based on baseline hippocampal MRI and 1-year follow-up cognitive measures using deep recurrent neural networks. CoRR abs/1901.01451 (2019)Google Scholar
  18. 18.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  19. 19.
    Luo, S., Li, X., Li, J.: Automatic Alzheimer’s disease recognition from MRI data using deep learning method. J. Appl. Math. Phys. 05, 1892–1898 (2017)CrossRefGoogle Scholar
  20. 20.
    Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst 29(6), 2063–2079 (2018)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Mahmud, M., Vassanelli, S.: Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges. Front. Neurosci. 10(JUN), 248 (2016). Scholar
  22. 22.
    Mahmud, M., Vassanelli, S.: Open-source tools for processing and analysis of in vitro extracellular neuronal signals. In: Chiappalone, M., Pasquale, V., Frega, M. (eds.) In Vitro Neuronal Networks. AN, vol. 22, pp. 233–250. Springer, Cham (2019). Scholar
  23. 23.
    Mathew, N.A., Vivek, R.S., Anurenjan, P.R.: Early diagnosis of Alzheimer’s disease from MRI images using PNN. In: Proceedings of the IC4, pp. 161–164 (2018)Google Scholar
  24. 24.
    Matsubara, T., et al.: Deep neural generative model of functional MRI images for psychiatric disorder diagnosis. IEEE Trans. Biomed. Eng. 66(10), 2768–79 (2019)CrossRefGoogle Scholar
  25. 25.
    Patel, P., Aggarwal, P., Gupta, A.: Classification of schizophrenia versus normal subjects using deep learning. In: Proceedings of the ICVGIP, India, pp. 281–286 (2016)Google Scholar
  26. 26.
    Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. CoRR abs/1502.02506 (2015)Google Scholar
  27. 27.
    Pinaya, W.H., et al.: Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci. Rep. 6, 38897 (2016)CrossRefGoogle Scholar
  28. 28.
    Poldrack, R., et al.: Computational and informatic advances for reproducible data analysis in neuroimaging. Annu. Rev. Biomed. Data Sci. 2, 119–138 (2019)CrossRefGoogle Scholar
  29. 29.
    Qi, J., Tejedor, J.: Deep multi-view representation learning for multi-modal features of the schizophrenia and schizo-affective disorder. In: Proceedings of the IEEE ICASSP, pp. 952–956 (2016)Google Scholar
  30. 30.
    Qiu, Y., et al.: Classification of schizophrenia patients and healthy controls using ICA of complex-valued fMRI data and convolutional neural networks. In: Lu, H., Tang, H., Wang, Z. (eds.) ISNN 2019. LNCS, vol. 11555, pp. 540–547. Springer, Cham (2019). Scholar
  31. 31.
    Qureshi, M.N.I., Oh, J., Lee, B.: 3D-CNN based discrimination of schizophrenia using resting-state fMRI. Artif. Intell. Med. 98, 10–17 (2019)CrossRefGoogle Scholar
  32. 32.
    Sarraf, S., Tofighi, G.: DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. bioRxiv (2016)Google Scholar
  33. 33.
    Sarraf, S., Tofighi, G.: Classification of Alzheimer’s disease using fMRI data and deep learning CNNs. CoRR abs/1603.08631 (2016)Google Scholar
  34. 34.
    Shatte, A., Hutchinson, D., Teague, S.: Machine learning in mental health: a scoping review of methods and applications. Psychol. Med. 49, 1–23 (2019)CrossRefGoogle Scholar
  35. 35.
    Shinde, S., et al.: Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage: Clin. 22, 101748 (2019)CrossRefGoogle Scholar
  36. 36.
    Sivaranjini, S., Sujatha, C.M.: Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimed. Tools Appl. (2019)Google Scholar
  37. 37.
    Spasov, S., et al.: A parameter-efficient DL approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. NeuroImage 189, 276–287 (2019)CrossRefGoogle Scholar
  38. 38.
    Srinivasagopalan, S., et al.: A deep learning approach for diagnosing schizophrenic patients. J. Exp. Theoret. Artif. Intell. 31, 1–14 (2019)CrossRefGoogle Scholar
  39. 39.
    Ullah, H.M.T., et al.: Alzheimer’s disease and dementia detection from 3D brain MRI data using deep CNNs. In: Proceedings of the I2CT 2018, pp. 1–3 (2018)Google Scholar
  40. 40.
    Ulloa, A., et al.: Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia. In: Proceedings of the IEEE MLSP, pp. 1–6 (2015)Google Scholar
  41. 41.
    Ulloa, A., Plis, S.M., Calhoun, V.D.: Improving classification rate of schizophrenia using a multimodal multi-layer perceptron model with structural and functional MR. CoRR abs/1804.04591 (2018)Google Scholar
  42. 42.
    Yan, W., et al.: Discriminating schizophrenia from normal controls using resting state functional network connectivity: a deep neural network and layer-wise relevance propagation method. In: Proceedings of the MLSP, pp. 1–6 (2017)Google Scholar
  43. 43.
    Zeng, L.L., et al.: Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine 30, 74–85 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Information TechnologyJahangirnagar UniversityDhakaBangladesh
  2. 2.Department of Computing and TechnologyNottingham Trent UniversityNottinghamUK

Personalised recommendations