Skip to main content
SpringerLink
Log in

Early detection of Alzheimer’s disease using squeeze and excitation network with local binary pattern descriptor

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Alzheimer’s disease is a degenerative brain disease that impairs memory, thinking skills, and the ability to perform even the most basic tasks. The primary challenge in this domain is accurate early stage disease detection. When the disease is detected at an early stage, medical professionals can prescribe medications to reduce brain shrinkage. Although the disease may not be curable, these interventions can extend the patient’s life by slowing down the rate of shrinkage. The four cognitive states of the human brain are cognitive normal (CN), mild cognitive impairment convertible (MCIc), mild cognitive impairment non-convertible (MCInc), and Alzheimer’s disease (AD). Mild cognitive impairment convertible (MCIc) is the early stage of Alzheimer’s disease. Individuals with MCIc will develop Alzheimer’s disease for a few years. However, it is difficult to detect this state through medical investigations. The mild cognitive impairment non-convertible state (MCInc) is the state immediately before MCIc. MCInc is a common condition in people of all ages, where minor memory issues arise as a result of normal aging. Early detection of AD can be claimed if and only if the transition from MCInc to MCIc is complete. Deep learning algorithms can be promising techniques for identifying the progression stage of a disease using magnetic resonance imaging. In this study, a novel deep learning algorithm was proposed to improve the classification accuracy of MCIc vs. MCInc. This study utilized the advantages of local binary patterns along with squeeze and excitation networks (SENet). Without the squeeze and excitation network, the classification accuracy of MCIc versus MCInc was 82%. The classification accuracy improved by 86% with the use of SENet. The experimental results show that the proposed model achieves better performance for MCInc vs. MCIc classification in terms of accuracy, precision, recall, F1 score, and ROC.

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
Fig. 8

Similar content being viewed by others

Data availability

The datasets used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. This was an open-access dataset available from the link (http://adni.loni.usc.edu).

References

  1. Breijyeh Z, Karaman R (2020) Comprehensive review on Alzheimer’s disease: causes and treatment. Molecules 25(24):5789. https://doi.org/10.3390/molecules25245789

    Article  Google Scholar 

  2. Mendez MF (2012) Early-onset Alzheimer’s disease: nonamnestic subtypes and type 2 AD. Arch Med Res 43(8):677–685. https://doi.org/10.1016/j.arcmed.2012.11.009

    Article  Google Scholar 

  3. Zhu D, Montagne A, Zhao Z (2021) Alzheimer’s pathogenic mechanisms and underlying sex difference. Cell Mol Life Sci 78(11):4907–4920

    Article  Google Scholar 

  4. Hien DTT, Huan HX, Huynh HT (2009) Multivariate interpolation using radial basis function networks. Int J Data Min Modelling Manage 1(3):291–309

    Google Scholar 

  5. Thusnavis Bella Mary I, Bruntha PM, Manimekalai MAP, Sagayam KM, Dang H (2021) Investigation of an efficient Integrated Semantic interactive algorithm for image Retrieval. Pattern Recognit Image Anal 31(4):709–721

    Article  Google Scholar 

  6. Hien DTT, Huan HX, Hoang LXM (2015) An effective solution to regression problem by RBF neuron network. Int J Oper Res Inform Syst (IJORIS) 6(4):57–74

    Article  Google Scholar 

  7. Rajesh G, Raajini XM et al (2020) A statistical approach for high order epistasis interaction detection for prediction of diabetic macular edema. Inf Med Unlocked 20:100362. https://doi.org/10.1016/j.imu.2020.100362

    Article  Google Scholar 

  8. Elayaraja P, Kumarganesh et al (2022) An efficient approach for detection and classification of cancer regions in cervical images using optimization based CNN classification approach. J Intell Fuzzy Syst 43(1):1023–1033

    Article  Google Scholar 

  9. Jebarani PE, Umadevi N et al (2021) A Novel Hybrid K-Means and GMM Machine learning model for breast Cancer detection. IEEE Access 9:146153–146162. https://doi.org/10.1109/ACCESS.2021.3123425

    Article  Google Scholar 

  10. Sundar GN, Narmadha D et al (2021) Automated sleep stage classification in sleep apnoea using convolutional neural networks, Informatics in Medicine Unlocked. Volume 26, 100724 (2021). https://doi.org/10.1016/j.imu.2021.100724

  11. Rajesh G, Raajini XM et al (2020) A statistical approach for high order epistasis interaction detection for prediction of diabetic macular edema. Inf Med Unlocked 20:100362

    Article  Google Scholar 

  12. Balwant MK (2022) A review on convolutional neural networks for Brain Tumor Segmentation: methods, datasets, libraries, and future directions. IRBM 43(6):521–537 -CNN

    Article  Google Scholar 

  13. Dequidt P, Bourdon P, Tremblais B, Guillevin C, Gianelli B, Boutet C, Cottier JP, Vallée JN, Fernandez-Maloigne C, Guillevin R (2021) Exploring radiologic criteria for glioma grade classification on the BraTS dataset. IRBM 42(6):407–414 – SVM, BRAIN TUMOR CLASIFICATION

    Article  Google Scholar 

  14. Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ (2022) A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. IRBM 43(4):290–299 – CNN AND SVM

    Article  Google Scholar 

  15. Angulakshmi M, Priya GL (2019) Walsh Hadamard transform for simple linear iterative clustering (SLIC) superpixel based spectral clustering of multimodal MRI brain tumor segmentation. IRBM 40(5):253–262 – MRI images, texture based super pixels

    Article  Google Scholar 

  16. Zhang Y, Duan J, Sa Y, Guo Y (2022) Multi-atlas based adaptive active contour model with application to organs at risk segmentation in brain mr images. IRBM 43(3):161–168 MRI images

    Article  Google Scholar 

  17. Singh VR (2008) Ultrasound hyperthermia control system for deep-seated tumours: ex vivo study of excised tumours, modeling of thermal profile and future nanoengineering aspects. IRBM 29(5):326–336 Medical application

    Article  Google Scholar 

  18. Wischhusen J, Padilla F (2019) Ultrasound-targeted microbubble destruction (UTMD) for localized drug delivery into tumor tissue. IRBM 40(1):10–15 MEDICAL APPICATION

    Article  Google Scholar 

  19. Caredda C, Mahieu-Williame L, Sablong R, Sdika M, Guyotat J, Montcel B (2021) Real time intraoperative functional brain mapping based on RGB Imaging. IRBM 42(3):189–197

    Article  Google Scholar 

  20. Dolz J, Massoptier L, Vermandel M (2015) Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: a survey. Irbm 36(4):200–212

    Article  Google Scholar 

  21. Gupta V, Mittal M, Mittal V, Sharma AK, Saxena NK (2021) A novel feature extraction-based ECG signal analysis. J Institution Eng (India): Ser B 102(5):903–913

    Article  Google Scholar 

  22. Gupta V, Mittal M, Mittal V (2022 May) A novel FrWT based arrhythmia detection in ECG signal using YWARA and PCA. Wireless Pers Commun 1:1–8

  23. Andrew J, Mhatesh et al (2021) Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder. Inf Med Unlocked 26:100713

    Article  Google Scholar 

  24. Andrushia AD, Sagayam KM et al (2021) Visual-saliency-based abnormality detection for MRI brain images-Alzheimer’s Disease Analysis. Appl Sci 11(19). https://doi.org/10.3390/app11199199

  25. Fathi S, Ahmadi M, Dehnad A (2022) Early diagnosis of Alzheimer’s disease based on deep learning: a systematic review. Comput Biol Med 105634. https://doi.org/10.1016/j.compbiomed.2022.105634

  26. Francis A, Pandian IA (2021) Early detection of Alzheimer’s disease using ensemble of pre- trained models. In2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) 2021 Mar 25, pp. 692–696. IEEE

  27. Suk HI, Shen D (2013) Deep learning-based feature representation for AD/MCI classification. In- International Conference on Medical Image Computing and Computer-Assisted Intervention 2013 Sep 22, pp. 583–590. Springer, Berlin, Heidelberg

  28. Payan A, Montana G (2013) Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv Preprint arXiv :150202506

  29. Gupta A, Ayhan M, Maida A (2013) Natural image bases to represent neuroimaging data. In International conference on machine learning 2013 Feb 13, pp. 987–994

  30. Valliani A, Soni A (2017) Deep residual nets for improved Alzheimer’s diagnosis. In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics 2017 Aug 20, pp. 615–615

  31. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

  32. McCrackin L (2018) Early detection of Alzheimer’s Disease using deep learning. In: Bagheri E, Cheung J (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture notes in Computer Science(), vol 10832. Springer, Cham, pp 355–359. https://doi.org/10.1007/978-3-319-89656-4_40.

    Chapter  Google Scholar 

  33. 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 

  34. Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X (2020) Early detection of Alzheimer’s Disease using magnetic resonance imaging: a Novel Approach combining Convolutional neural networks and ensemble learning. Front NeuroSci, 14

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

    Article  Google Scholar 

  36. Oh K, Chung YC, Kim KW et al (2019) Classification and visualization of Alzheimer’s Disease using volumetric convolutional neural network and transfer learning. Sci Rep 9:18150. https://doi.org/10.1038/s41598-019-54548-6

    Article  Google Scholar 

  37. Huang Y, Xu J, Zhou Y, Tong T, Zhuang X, Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2019) Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network. Front NeuroSci 13:509

    Article  Google Scholar 

  38. Sun J, Yan S, Song C, Han B (2020) Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer’s disease. Int J Comput Assist Radiol Surg 15(3):445–455

    Article  Google Scholar 

  39. Al-Shoukry S, Rassem TH, Makbol NM (2020) Alzheimer’s diseases detection by using Deep Learning algorithms: a Mini-review. IEEE Access 8:77131–77141

    Article  Google Scholar 

  40. Ju R, Hu C, Li Q (2017) Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans Comput Biol Bioinf 16(1):244–257

    Article  Google Scholar 

  41. Wen J, Thibeau-Sutre E, Diaz-Melo M et al (2020) Alzheimer’s Disease Neuroimaging Initiative. Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Med Image Anal 63:101694. https://doi.org/10.1016/j.media.2020.101694

    Article  Google Scholar 

  42. Yan S, Song C, Zheng B (2019) 3D local directional patterns for early diagnosis of Alzheimer’s disease. J Eng 2019(14):530–535

    Google Scholar 

  43. Shao W, Peng Y, Zu C, Wang M, Zhang D (2020) Alzheimer’s Disease Neuroimaging Initiative. Hypergraph based multi-task feature selection for multimodal classification of Alzheimer’s disease. Comput Med Imaging Graph 80:101663

    Article  Google Scholar 

  44. Nanni L et al (2019) Texture descriptors and voxels for the early diagnosis of Alzheimer’s disease. Artif Intell Med 97:19–26

    Article  Google Scholar 

  45. Luk CC et al (2018) Alzheimer’s disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment. Alzheimer’s Dementia: Diagnosis Assess Disease Monit 10:755–763. https://doi.org/10.1016/j.dadm.2018.09.002

    Article  Google Scholar 

  46. C¸ evik A et al (2017) Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI. Annals of Operations Research 258.1 : 31–57 (2017)

  47. Nanni L, Brahnam S, Maguolo G (2019) Data augmentation for building an ensemble of convolutional neural networks. Innovation in Medicine and Healthcare Systems, and Multimedia. Springer, Singapore, pp 61–69

    Google Scholar 

  48. Chincarini A, Bosco P, Gemme G et al (2014) Automatic temporal lobe atrophy assessment in prodromal AD: data from the DESCRIPA study. Alzheimer’s Dement 10(4):456–467

    Article  Google Scholar 

  49. Maguolo G, Nanni L, Ghidoni S (2019) Ensemble of Convolutional Neural Networks Trained with different activation functions. arXiv preprint arXiv:1905.02473

  50. Nanni L, Ghidoni S, Brahnam S (2021) Ensemble of convolutional neural networks for bioimage classification. Appl Comput Inf 17(1):19–35. https://doi.org/10.1016/j.aci.2018.06.002

    Article  Google Scholar 

  51. Liu M et al (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuro Image 208 : 116459 (2020). https://doi.org/10.1016/j.neuroimage.2019.116459

  52. Kavitha C, Mani V, Srividhya SR et al (2022) Early-stage Alzheimer’s Disease Prediction using machine learning models. Front Public Health 10. https://doi.org/10.3389/fpubh.2022.853294

  53. Francis A, and Immanuel Alex Pandian (2018). Review on Local Feature Descriptors for Early Detection of Alzheimer’s Disease. 2018 International Conference on CircuitsSystems in Digital Enterprise Technology (ICCSDET). IEEE

  54. François C (2015) Keras: The Python deep learning library. keras. io : 86

  55. Chollet F et al (2021) Keras (Version 2.6) [Computer software]. GitHub

  56. Huang Y, Xu J, Zhou Y, Tong T, Zhuang X, Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2019). Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network. Front NeuroSci, 509

  57. Francis A, Pandian IA (2021) Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network. Multimedia Tools Appl 80(19):29585–29600

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank all of our universities, institutes, and organizations for facilitating our time support in this study.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    Ambily Francis, S. Immanuel Alex Pandian & K. Martin Sagayam

  2. Department of Electronics and Communication, Sahrdaya College of Engineering and Technology, Kerala, India

    Ambily Francis

  3. Department of Computer Science, INSA Lyon, Villeurbanne, France

    Lam Dang

  4. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    J. Anitha

  5. Department of Information Systems, Suffolk University, Boston, USA

    Linh Dinh

  6. Department of Computer Science, University of Massachusetts, Boston, MA, USA

    Marc Pomplun

  7. Department of Mathematics and Computer Science, Molloy University, Rockville Centre, NY, USA

    Hien Dang

  8. Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam

    Hien Dang

Authors
  1. Ambily Francis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Immanuel Alex Pandian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Martin Sagayam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lam Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. Anitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Linh Dinh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marc Pomplun
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hien Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Contributors A.F., I.A.P and H.D. conceived and designed the research, and A.F., I.A.P, A.J, K.M.S and H.D. wrote the first draft of the manuscript. L.D., M.P and Li.D. all contributed in a substantial way to the writing process. All the authors revised the manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Hien Dang.

Ethics declarations

Ethical and informed consent

Not applicable.

Competing interests

The authors declare that they have no competing.

Additional information

Publisher’s Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Francis, A., Pandian, S., Sagayam, K. et al. Early detection of Alzheimer’s disease using squeeze and excitation network with local binary pattern descriptor. Pattern Anal Applic 27, 54 (2024). https://doi.org/10.1007/s10044-024-01280-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10044-024-01280-1

Keywords

Search

Navigation