Top

Published in:

2022 | OriginalPaper | Chapter

48. Identification of Alzheimer’s Disease Using Various Deep Learning Techniques—A Review

Authors : Ragavamsi Davuluri, Ragupathy Rengaswamy

Published in: Intelligent Manufacturing and Energy Sustainability

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Effective identification of Alzheimer’s disease (AD) becomes primary importance in biomedical research. Recently, deep models have reported with high accuracy for AD detection compared to general machine learning (ML) techniques. Nevertheless, identifying brain disorder like AD and tumor is still challenging, and for classification, it requires a highly discriminative feature representation to separate similar brain patterns. Present state of AD detection techniques using various deep learning (DL) models is studied here. Features of personal data, genetic information, and brain scans were focused. This includes required preprocessing steps with neuro-imaging data that extracted from single and multiple modalities. Performance of deep learning mechanisms and their accuracy values obtained are described. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially regarding the availability of datasets and training procedures.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now

previous chapter Design and Analysis of Multi-Tool Light Agricultural Vehicle

next chapter UPQC with AI Techniques for Power Quality Improvement

Ilias, M., Lazaros, I., Elias, P.: Applying deep learning to predicting. Artif. Intell. Appl. Innovations 584, 308–319 (2020)CrossRef

William, T.: 2018 Alzheimer’s disease facts and figures. Alzheimers Dement 14(3), 367–429 (2018)MathSciNetCrossRef

Ker, J., Wang, L.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2017)CrossRef

Litjens, G., Kooi, T., Ehteshami, B.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRef

Ragavamsi, D., Ragupathy, R.: A survey of different machine learning models for Alzheimer disease prediction. Int. J. Emerg. Trends Eng. Res. 8, 3328–3337 (2020)CrossRef

Liu, J., Pan, Y., Li, M.: Applications of deep learning to MRI images: a survey. Big Data Mining Analytics 1(1), 1–18 (2018)CrossRef

Lu, D., Karthik, P., Rakesh, B.: Alzheimer’s disease neuroimaging initiative multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci. Rep. 8, 5697 (2018)CrossRef

Akkus, Z., Hoogi, A., Rubin, D.L.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imag. 30, 449–459 (2017)

Liu, M.: Deep multi-task multi-channel learning for joint classification and regression of brain status. In:: International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS, pp 3–11. Springer (2017)

10.

Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17, 87–97 (1998)CrossRef

11.

Jack, C.R., Jr., Wiste, H.J., Prasanthi, V.: Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer’s disease. Brain 133, 3336–3348 (2010)CrossRef

12.

Vu, T.D., Ho, N.H., Yang, H.J.: Non-white matter tissue extraction and deep convolutional neural network for Alzheimer’s disease detection. Soft. Comput. 22, 6825–6833 (2018)CrossRef

13.

Ortiz, A., Gorriz, J.M.: Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Int. J. Neural Syst. 26, 1650025 (2016)CrossRef

14.

Çitak-ER, F., Goularas, D., Ormeci, B.: A novel convolutional neural network model based on voxel-based morphometry of imaging data in predicting the prognosis of patients with mild cognitive impairment. J. Neurol. Sci. 34, 52–69 (2017)

15.

Farooq, A., Awais, M.: 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, China, pp 1–6 (2017)

16.

Sarraf, S., Tofighi, G.: Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. arXiv preprint

17.

Sarraf, S., Tofighi, G.: Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data. In: 2016 Future Technologies Conference (FTC), pp 816–820, USA (2016)

18.

Wu, C., Guo, S., Hong, Y.: Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks. Quant. Imaging Med. Surg. 8, 992–1003 (2018)CrossRef

19.

Hon, M., Khan, N.: Towards Alzheimer’s disease classification through transfer learning. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1166–1169 (2017)

20.

Tzourio-Mazoyer, N., Landeau, B., Crivello, F.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002)CrossRef

21.

Liu, S., Cai, W., Che, H.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62, 1132–1140 (2014)CrossRef

22.

Li. F., Tran, L., Ji, S.: A robust deep model for improved classification of AD/MCI patients. IEEE J. Biomed. Health Inf. 19, 1610–1616 (2015)

23.

Ju, R., Pan Zhuo, C., Li, Q.: Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans. Comput. Biol. Bioinf. 16, 244–257 (2017)CrossRef

24.

Khvostikov, A., Aderghal, K., Benois-Pineau, J.: 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies, arXiv preprint

25.

Cui, R., Liu, M.: Hippocampus analysis based on 3D CNN for Alzheimer’s disease diagnosis. In: Tenth International Conference on Digital Image Processing (ICDIP 2018), International Society for Optics and Photonics, pp 108065 (2018)

26.

Gupta, A., Anthony, S.M., Ayhan, S.: Natural image bases to represent neuroimaging data. In: International Conference on Machine Learning, pp 987–994, USA (2013)

27.

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

28.

Li, F., Liu, M.: Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks. Comput. Med. Imaging Graph. 70, 101–110 (2018)CrossRef

29.

Shakeri, M., Lombaert, H., Shashank, T.: Deep spectral-based shape features for Alzheimer’s disease classification. In: International Workshop on Spectral and Shape Analysis in Medical Imaging. LNCS, pp 15–24 (2016)

30.

Wolz, R.: Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PloS one (2013)

31.

Bhatkoti, P., Paul, M.: Early diagnosis of Alzheimer’s disease: a multi-class deep learning framework with modified k-sparse auto encoder classification. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, New Zealand, pp 1–5 (2016)

32.

Livni, R., Shalev-Shwartz, S., Shamir, O.: An algorithm for training polynomial networks, arXiv preprint

33.

Zheng, X., Shi, J., Li, Y., Liu, X.: Multi-modality stacked deep polynomial network based feature learning for Alzheimer’s disease diagnosis. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE, Czech Republic, pp 851–854 (2016)

34.

Razzak, M., Naz, S., Zaid, A.: Deep learning for medical image processing: overview, challenges and the future. Classif. BioApps 26, 323–350 (2017)CrossRef

35.

Danni, C., Manhua, L.: CNNs based multi-modality classification for AD diagnosis. In: 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), IEEE, pp 1–5 (2017)

36.

Luo, S., Li, X., Li, J.: Automatic Alzheimer’s disease recognition from MRI data using deep learning method. J. Appl. Math. Phys. 5, 1892–1898 (2017)CrossRef

37.

Suk, H.I., Lee, S.W., Shen, D.: Deep ensemble learning of sparse regression models for brain disease diagnosis. Med. Image Anal. 37, 101–113 (2017) CrossRef

38.

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

39.

Liu, M., Li, F.: Alzheimer’s disease classification based on combination of multi-model convolutional networks. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST), IEEE, China, pp 1–5 (2017)

40.

Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks, arXiv preprint

41.

Cheng, D., Liu, M., Fu, J., Wang, Y.: Classification of MR brain images by combination of multi-CNNs for AD diagnosis. In: Ninth International Conference on Digital Image Processing (ICDIP 2017), International Society for Optics and Photonics, p 1042042 (2017)

42.

Karasawa, H., Liu, C., Ohwada, H.: Deep 3d convolutional neural network architectures for Alzheimer’s disease diagnosis. In: Asian Conference on Intelligent Information and Database System, LNCS, pp 287–296. Springer (2018)

43.

Liu, S., Cheng, D., Wang, K.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62, 1132–1140 (2014)CrossRef

44.

Cui, R., Liu, M., Li, G.: Longitudinal analysis for Alzheimer’s disease diagnosis using RNN. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, USA, pp 1398–1401 (2018)

45.

Suk, H.I., Lee, S.W., Shen, D.: Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis. Brain Struct. Funct. 221, 2569–2587 (2016)CrossRef

46.

Lu, D., Karteek, P., Ding, G.W.: Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. Med. Image Anal. 46, 26–34 (2018)CrossRef

47.

Li, F., Tran, L., Thung, K.H.: A robust deep model for improved classification of AD/MCI patients. IEEE J. Biomed. Health Inform. 19, 1610–1616 (2015)CrossRef

48.

Faturrahman, M., Hanifah, N., Wasito, I.: Structural MRI classification for Alzheimer’s disease detection using deep belief network. In: 2017 11th International Conference on Information and Communication Technology and System (ICTS), IEEE, Indonesia, pp 37–42 (2017)

49.

Suk, H.I., Lee, W., Shen, D.: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101, 569–582 (2014)CrossRef

50.

Shi, J., Zheng, X., Li, Y.: Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE J. Biomed. Health Inform. 22, 173–183 (2017)CrossRef

51.

Wang, S.H., Phillips, P., Sui, Y.: Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J. Med. Syst. 42, 85 (2018)CrossRef

52.

Liu, M., Zhang, J., Adeli, E., Shen, D.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157–168 (2018)CrossRef

53.

Choi, H., Jin, K.H., Kyong, C.: Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav. Brain Res. 344, 103–109 (2018)CrossRef

54.

Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP), IEEE, pp 126–130 (2016)

55.

Korolev, S., Safiullin, A., Belyaev, M., Dodonova, Y.: Residual and plain convolutional neural networks for 3D brain MRI classification. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), IEEE, Australia, pp 835–838 (2017)

56.

Lu, D., Karteek, P., Ding, G.W.: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci. Rep. 8, 1–13 (2018)

57.

Sarraf, S., DeSouza, D., Anderson, J., Tofighi, G.: DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI, BioRxiv

Title: Identification of Alzheimer’s Disease Using Various Deep Learning Techniques—A Review
Authors: Ragavamsi Davuluri
Ragupathy Rengaswamy
Publisher: Springer Singapore
Book: Intelligent Manufacturing and Energy Sustainability
Print ISBN: 978-981-16-6481-6

Electronic ISBN: 978-981-16-6482-3

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-981-16-6482-3_48

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partners