Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
EEG Signal Indicator for Emotional Reactivity Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Manan Binth Taj Noor, Nusrat Zerin Zenia, M. Shamim Kaiser, Mufti Mahmud, Shamim Al Mamun

Erschienen in: Brain Informatics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Image-Assisted Discrimination Method for Neurodevelopmental Disorders in Infants Based on Multi-feature Fusion and Ensemble Learning
Nächstes Kapitel The Changes of Brain Networks Topology in Graph Theory of rt-fMRI Emotion Self-regulation Training
Literatur
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)CrossRef
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)CrossRef
3.
Bohle, M.: Layer-wise relevance propagation for explaining DNN decisions in MRI-based Alzheimer’s disease classification. Front. Aging Neurosci. 11, 194 (2019)CrossRef
4.
Brown, C.J., Hamarneh, G.: Machine learning on human connectome data from MRI. CoRR abs/1611.08699 (2016)
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)
6.
Dakka, J., et al.: Learning neural markers of schizophrenia disorder using recurrent neural networks. CoRR abs/1712.00512 (2017)
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)
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)
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)
10.
Gottapu, R.D., Dagli, C.H.: Analysis of Parkinson’s disease data. Proc. Comput. Sci. 140, 334–341 (2018)CrossRef
11.
Han, S., et al.: Recognition of early-onset schizophrenia using deep-learning method. Appl. Inform. 4(1), 16 (2017)CrossRef
12.
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)CrossRef
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)
15.
Kollias, D., et al.: Deep neural architectures for prediction in healthcare. Complex Intell. Syst. 4(2), 119–131 (2018)MathSciNetCrossRef
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)CrossRef
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)
18.
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRef
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)CrossRef
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)MathSciNetCrossRef
21.
22.
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)
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)CrossRef
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)
26.
Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. CoRR abs/1502.02506 (2015)
27.
Pinaya, W.H., et al.: Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci. Rep. 6, 38897 (2016)CrossRef
28.
Poldrack, R., et al.: Computational and informatic advances for reproducible data analysis in neuroimaging. Annu. Rev. Biomed. Data Sci. 2, 119–138 (2019)CrossRef
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)
30.
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)CrossRef
32.
Sarraf, S., Tofighi, G.: DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. bioRxiv (2016)
33.
Sarraf, S., Tofighi, G.: Classification of Alzheimer’s disease using fMRI data and deep learning CNNs. CoRR abs/1603.08631 (2016)
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)CrossRef
35.
Shinde, S., et al.: Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage: Clin. 22, 101748 (2019)CrossRef
36.
Sivaranjini, S., Sujatha, C.M.: Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimed. Tools Appl. (2019)
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)CrossRef
38.
Srinivasagopalan, S., et al.: A deep learning approach for diagnosing schizophrenic patients. J. Exp. Theoret. Artif. Intell. 31, 1–14 (2019)CrossRef
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)
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)
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)
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)
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)CrossRef
Metadaten
Titel
Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective
verfasst von
Manan Binth Taj Noor
Nusrat Zerin Zenia
M. Shamim Kaiser
Mufti Mahmud
Shamim Al Mamun
Copyright-Jahr
2019
Buch
Brain Informatics

Print ISBN: 978-3-030-37077-0

Electronic ISBN: 978-3-030-37078-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-37078-7_12