Skip to main content

Advertisement

SpringerLink
Log in

A CAD system design to diagnosize alzheimers disease from MRI brain images using optimal deep neural network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Memory related issues in brain are mainly caused by Alzheimer disease (AD) which is the most common form of dementia. This disease must be diagnosed in its prodromal stage known as Mild Cognitive Impairment (MCI) also it needs an accurate detection and classification technique. In this paper, a computer-aided diagnosis (CAD) system is implemented on Magnetic resonance imaging (MRI) data from ADNI database. This disease highly affects the Hippocampus and cerebrum regions which are normally found in the grey matter region of brain. At first, MNI/ICBM atlas space of every three dimensional MRI images are constructed using normalization procedure, then grey matter region of brain is extracted. Subsequently, feature extraction is done by two dimensional Gabor filter in three scales and eight orientations. Then, the proposed optimal Deep Neural Network (DNN) classifier is used to classify the images as Cognitive normal (CN), Alzheimer disease (AD), and Mild Cognitive Impairment (MCI). Here, DNN classifier is optimized by selecting optimal weight parameter using Enhanced Squirrel Search Algorithm. The experimental results prove an efficiency of the proposed method using MR images. The proposed algorithm beats existing techniques in terms of accuracy, sensitivity, and specificity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ahmed OB, Benois-Pineau J, Allard M, Amar CB, Catheline G, Alzheimer’s Disease Neuroimaging Initiative (2015) Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features. Multimed Tools Appl 74(4):1249–1266

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Babulal GM, Quiroz YT, Albensi BC, Arenaza-Urquijo E, Astell AJ, Babiloni C, Bahar-Fuchs A et al (2019) Perspectives on ethnic and racial disparities in Alzheimer’s disease and related dementias: Update and areas of immediate need. Alzheimer’s Dement 15(2):292–312

    Article  Google Scholar 

  4. Bartos A, Gregus D, Ibrahim I, Tintěra J (2019) Brain volumes and their ratios in Alzheimer´ s disease on magnetic resonance imaging segmented using Freesurfer 6.0. Neuroimaging, Psychiatry Research

    Book  Google Scholar 

  5. Bilderbeck AC, Penninx BWJH, Arango C, van der Wee N, Kahn R, Rossum I W-v, Hayen A, Kas MJ, Post A, Dawson GR (2019) Overview of the clinical implementation of a study exploring social withdrawal in patients with schizophrenia and Alzheimer’s disease. Neurosci Biobehav Rev 97:87–93

    Article  Google Scholar 

  6. Çevik A, Weber G-W, Eyüboğlu BM, Oğuz KK, Alzheimer’s Disease Neuroimaging Initiative (2017) Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI. Ann Oper Res 258(1):31–57

    Article  MathSciNet  Google Scholar 

  7. Cui R, Liu M, Initiative A's DN (2019) RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Comput Med Imaging Graph 73:1–10

    Article  Google Scholar 

  8. 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

  9. Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175

    Article  Google Scholar 

  10. Ju R, Hu C, Pan Z, Li Q (2019) Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans Comput Biol Bioinf (TCBB) 16(1):244–257

    Article  Google Scholar 

  11. Karami V, Nittari G, Amenta F (2019) Neuroimaging computer-aided diagnosis systems for Alzheimer’s disease. Int J Imaging Syst Technol 29(1):83–94

    Article  Google Scholar 

  12. Keserwani P, Pammi V S C, Prakash O, Khare A, Jeon M (2016) Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region. Int J Image Graph Sig Process 8, no. 6

  13. Li H-C, Chen P-Y, Cheng H-F, Kuo Y-M, Huang C-C (2019) In vivo visualization of brain vasculature in Alzheimer’s disease mice by high-frequency micro-Doppler imaging. IEEE Trans Biomed Eng 66:3393–3401

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Lin S-Y, Lin C-P, Hsieh T-J, Lin C-F, Chen S-H, Chao Y-P, Chen Y-S, Hsu C-C, Kuo L-W (2019) Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer’s disease. NeuroImage: Clinical 22:101680

    Article  Google Scholar 

  16. Liu J, Li M, Lan W, Wu F-X, Pan Y, Wang J (2016) Classification of alzheimer's disease using whole brain hierarchical network. IEEE/ACM Trans Comput Biol Bioinforma 15(2):624–632

    Article  Google Scholar 

  17. Liu J, Wang J, Hu B, Wu F-X, Pan Y (2017) Alzheimer’s disease classification based on individual hierarchical networks constructed with 3-D texture features. IEEE Trans Nanobiosci 16(6):428–437

    Article  Google Scholar 

  18. Liu M, Zhang J, Adeli E, Shen D (2018) Joint classification and regression via deep multi-task Multi-Channel learning for Alzheimer's disease diagnosis. IEEE Trans Biomed Eng 66(5):1195–1206

  19. Liu M, Zhang J, Lian C, Shen D (2019) Weakly supervised deep learning for brain disease prognosis using MRI and incomplete clinical scores. IEEE Trans Cybern:1–12

  20. Meyer SRA, De Jonghe JFM, Schmand B, Ponds RWHM (2019) Visual associations to retrieve episodic memory across healthy elderly, mild cognitive impairment, and patients with Alzheimer’s disease. Aging Neuropsychol Cognit 26(3):447–462

    Article  Google Scholar 

  21. Pandya MD, Shah PD, Jardosh S (2019) Medical image diagnosis for disease detection: A deep learning approach. In U-Healthcare Monitoring Systems, pp. 37–60. Academic Press

  22. Peng J, Zhu X, Wang Y, An L, Shen D (2019) Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis. Pattern Recogn 88:370–382

    Article  Google Scholar 

  23. Platero C, López ME, del Carmen Tobar M, Yus M, Maestu F (2019) Discriminating Alzheimer's disease progression using a new hippocampal marker from T1-weighted MRI: The local surface roughness. Hum Brain Mapp 40(5):1666–1676

    Article  Google Scholar 

  24. Razavi F, Tarokh MJ, Alborzi M (2019) An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning. J Big Data 6(1):32

    Article  Google Scholar 

  25. Saravanakumar S, Thangaraj P (2019) A computer aided diagnosis system for identifying Alzheimer’s from MRI scan using improved Adaboost. J Med Syst 43(3):76

    Article  Google Scholar 

  26. Shi Y, Suk H-I, Yang G, Lee S-W, Shen D (2019) Leveraging coupled interaction for multimodal Alzheimer's disease diagnosis. IEEE Trans Neural Netw Learn Syst

  27. Wang H, Shen Y, Wang S, Xiao T, Deng L, Wang X, Zhao X (2019) Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing 333:145–156

    Article  Google Scholar 

  28. Wang S-H, Zhang Y, Li Y-J, Jia W-J, Liu F-Y, Yang M-M, Zhang Y-D (2018) Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimed Tools Appl:1–25

  29. Xu L, Yao Z, Li J, Lv C, Zhang H, Bin H (2019) Sparse feature learning with label information for Alzheimer’s disease classification based on magnetic resonance imaging. IEEE Access 7:26157–26167

    Article  Google Scholar 

  30. 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 

  31. Zhang Y, Zhang E, Chen W (2016) Deep neural network for halftone image classification based on sparse auto-encoder. Eng Appl Artif Intell 50:245–255

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, University College of Engineering, JNTUK, Kakinada, India

    Pemmu Raghavaiah

  2. Department of Electronics and Communication Engineering, SVU College of Engineering, Sri Venkateswara University, Tirupati, India

    S. Varadarajan

Authors
  1. Pemmu Raghavaiah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Varadarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pemmu Raghavaiah.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raghavaiah, P., Varadarajan, S. A CAD system design to diagnosize alzheimers disease from MRI brain images using optimal deep neural network. Multimed Tools Appl 80, 26411–26428 (2021). https://doi.org/10.1007/s11042-021-10928-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10928-7

Keywords

Search

Navigation