EADDA: Towards Novel and Explainable Deep Learning for Early Alzheimer's Disease Diagnosis Using Autoencoders

Authors

  • Mohammed Raza Syed Dept. of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India
  • Neel Kothari Dept. of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India
  • Yash Joshi Dept. of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India
  • Aruna Gawade Dept. of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India

Keywords:

Early Alzheimer’s disease (AD), ADNI 5-class, Deep Learning, Autoencoders, Classification, Neuroimaging

Abstract

According to the WHO, Alzheimer's disease (AD) is the seventh most common cause of death worldwide as of 2023. The early identification of AD is difficult, and there are currently no known preventative procedures. It is crucial to develop an accurate computer-aided system for the early detection of AD to help AD patients. One of the most promising areas for the early identification of Alzheimer's disease is neuroimaging, and early diagnosis is crucial for determining the creation and efficacy of treatment alternatives. To do so, the authors propose a novel architecture which is a Deep-learning centric, computationally efficient and is an integrated Early Alzheimer's Disease detection system. A joint autoencoder-latent vector-based classification system is proposed. Specifically, a convolutional autoencoder is used to generate a latent vector. This latent vector is further passed through a Latent Classifier module (LCM) to be classified using the Deep Parallel Ensemble (DPE), consisting of 5 base classification models: SVM, Random Forest (RF), Extra-Trees Classifier (ETC), XGBoost (XGB), and Multi-Layer Perceptron (MLP). The system is trained and tested on a 5-class Alzheimer’s dataset consisting of high-resolution MRI images. The proposed system “EADDA” gives a testing accuracy of 86.57%, being the only work exploring and experimenting with the ADNI 5-class dataset.

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References

N. I. on Aging (NIA), “Alzheimer’s Disease Genetics Fact Sheet,” National Institute on Aging. 2019.

“Alzheimer’s disease international (ADI),” https://www.alzint.org/, May 18, 2023.

“Dementia World Health Organization. Available,” https://www.who.int/news-room/fact-sheets/detail/dementia, May 19, 2023.

Alzheimer’s Disease and Healthy Aging, “Alzheimer’s Disease and Related Dementias,” https://www.cdc.gov/aging/aginginfo/alzheimers.htm, 2023.

“Mini-Mental State Exam (MMSE) Test for Alzheimer’s Dementia. ,” https://www.dementiacarecentral.com/mini-mental-state-exam, Jul. 26, 2023.

J. K. Kueper, M. Speechley, and M. Montero-Odasso, “The Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Modifications and Responsiveness in Pre-Dementia Populations. A Narrative Review,” Journal of Alzheimer’s Disease, vol. 63, no. 2. 2018. doi: 10.3233/JAD-170991.

M. F. and D. M. H. R. Craig-Schapiro, “Biomarkers of Alzheimer’s disease,” Neurobiol. Disease.

R. Jain, N. Jain, A. Aggarwal, and D. J. Hemanth, “Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images,” Cogn Syst Res, vol. 57, 2019, doi: 10.1016/j.cogsys.2018.12.015.

G. Uysal and M. Ozturk, “Classifying Early and Late Mild Cognitive Impairment Stages of Alzheimer’s Disease by Analyzing Different Brain Areas,” in TIPTEKNO 2020 - Tip Teknolojileri Kongresi - 2020 Medical Technologies Congress, TIPTEKNO 2020, 2020. doi: 10.1109/TIPTEKNO50054.2020.9299217.

K. A. N. N. P. Gunawardena, R. N. Rajapakse, and N. D. Kodikara, “Applying convolutional neural networks for pre-detection of Alzheimer’s disease from structural MRI data,” in 2017 24th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2017, 2017. doi: 10.1109/M2VIP.2017.8211486.

S. S. Rajeswari and M. Nair, “A Transfer Learning Approach for Predicting Alzheimer’s Disease,” in 2021 International Conference on Nascent Technologies in Engineering, ICNET 2021 - Proceedings, 2021. doi: 10.1109/ICNTE51185.2021.9487746.

H. D. Alon, M. A. D. Ligayo, M. A. Misola, A. A. Sandoval, and M. V. Fontanilla, “Eye-Zheimer: A Deep Transfer Learning Approach of Dementia Detection and Classification from NeuroImaging,” in 7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020, 2020. doi: 10.1109/ICETAS51660.2020.9484315.

J. Islam and Y. Zhang, “Early diagnosis of alzheimer’s disease: A neuroimaging study with deep learning architectures,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi: 10.1109/CVPRW.2018.00247.

P C Muhammed Raees and Vinu Thomas, “J. Phys.: Conf. Ser.,” 1921 012024. DOI 10.1088/1742-6596/1921/1/012024, 2021.

Ebrahimi, S. Luo, and for the A. Disease Neuroimaging Initiative, “Convolutional neural networks for Alzheimer’s disease detection on MRI images,” Journal of Medical Imaging, vol. 8, no. 02, 2021, doi: 10.1117/1.jmi.8.2.024503.

D. H. Chaihtra and S. Vijaya Shetty, “Alzheimer’s Disease Detection from Brain MRI Data using Deep Learning Techniques,” in 2021 2nd Global Conference for Advancement in Technology, GCAT 2021, 2021. doi: 10.1109/GCAT52182.2021.9587756.

M. Mamun, S. Bin Shawkat, M. S. Ahammed, M. M. Uddin, M. I. Mahmud, and A. M. Islam, “Deep Learning Based Model for Alzheimer’s Disease Detection Using Brain MRI Images,” in 2022 IEEE 13th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2022, 2022. doi: 10.1109/UEMCON54665.2022.9965730.

H. A. Helaly, M. Badawy, and A. Y. Haikal, “Deep Learning Approach for Early Detection of Alzheimer’s Disease,” Cognit Comput, vol. 14, no. 5, 2022, doi: 10.1007/s12559-021-09946-2.

F. U. R. Faisal and G. R. Kwon, “Automated Detection of Alzheimer-s Disease and Mild Cognitive Impairment Using Whole Brain MRI,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3180073.

S. Korolev, A. Safiullin, M. Belyaev, and Y. Dodonova, “Residual and plain convolutional neural networks for 3D brain MRI classification,” in Proceedings - International Symposium on Biomedical Imaging, 2017. doi: 10.1109/ISBI.2017.7950647.

T. Zheng-Lin, W. Hai-Xing, Y. Shao-Xun, S. Xiao, and X. Jian-Ming, “Classification of Alzheimer’s Disease Based on Stacked Denoising Autoencoder,” in Proceedings - 2018 4th Annual International Conference on Network and Information Systems for Computers, ICNISC 2018, 2018. doi: 10.1109/ICNISC.2018.00056.

E. Ç. Polat and A. Güvenis, “Detecting Alzheimer Disease on FDG PET Images Using a Similarity Index Based on Mutual Information,” in TIPTEKNO 2020 - Tip Teknolojileri Kongresi - 2020 Medical Technologies Congress, TIPTEKNO 2020, 2020. doi: 10.1109/TIPTEKNO50054.2020.9299268.

M. Tanveer, A. H. Rashid, M. A. Ganaie, M. Reza, I. Razzak, and K. L. Hua, “Classification of Alzheimer’s Disease Using Ensemble of Deep Neural Networks Trained Through Transfer Learning,” IEEE J Biomed Health Inform, vol. 26, no. 4, 2022, doi: 10.1109/JBHI.2021.3083274.

Gamal, M. Elattar, and S. Selim, “Automatic Early Diagnosis of Alzheimer’s Disease Using 3D Deep Ensemble Approach,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3218621.

H. Lao and X. Zhang, “Regression and Classification of Alzheimer’s Disease Diagnosis Using NMF-TDNet Features from 3D Brain MR Image,” IEEE J Biomed Health Inform, vol. 26, no. 3, 2022, doi: 10.1109/JBHI.2021.3113668.

M. F. Folstein, S. E. Folstein, and P. R. McHugh, “‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician,” J Psychiatr Res, vol. 12, no. 3, 1975, doi: 10.1016/0022-3956(75)90026-6.

J. C. Morris, “The clinical dementia rating (cdr): Current version and scoring rules,” Neurology, vol. 43, no. 11, 1993, doi: 10.1212/wnl.43.11.2412-a.

Howard, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” Computer Vision and Pattern Recognition, 2017.

M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in 36th International Conference on Machine Learning, ICML 2019, 2019.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016. doi: 10.1109/CVPR.2016.90.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017. doi: 10.1109/CVPR.2017.243.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.

W. Lin et al., “Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment,” Front Neurosci, vol. 12, no. NOV, 2018, doi: 10.3389/fnins.2018.00777.

C. Cortes and V. Vapnik, “Support-Vector Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273–297. http://doi.org/10.1023/A:1022627411411Networks,” Mach Learn, vol. 20, no. 3, 1995.

L. Breiman, “Random Forests,” Mach Learn, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.

P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn, vol. 63, no. 1, pp. 3–42, Apr. 2006, doi: 10.1007/s10994-006-6226-1.

J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Ann Stat, vol. 29, no. 5, 2001, doi: 10.1214/aos/1013203451.

M. Ohbayashi and K. Hirasawa, “Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences,” Ph. D. dissertation, Harvard University, vol. 117, no. 3, 1974.

Rodriguez-Baca, L. S. ., Allagi, S. ., Larrea-Serquen, R. ., Cruzado, C. F. ., Diaz, M. A. ., Garcia-Hernández, S. ., & Monteiro, J. D. . (2023). Experimental Study based on the Implementation of a Regulatory Framework for the Improvement of Cyber Resilience in SMEs. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 199–205. https://doi.org/10.17762/ijritcc.v11i3.6337

Ahmed Abdelaziz, Machine Learning Approaches for Predicting Stock Market Volatility , Machine Learning Applications Conference Proceedings, Vol 3 2023.

Chaudhury, S., Dhabliya, D., Madan, S., Chakrabarti, S. Blockchain technology: A global provider of digital technology and services (2023) Building Secure Business Models Through Blockchain Technology: Tactics, Methods, Limitations, and Performance, pp. 168-193.

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Published

21.09.2023

How to Cite

Syed, M. R. ., Kothari, N. ., Joshi, Y. ., & Gawade, A. . (2023). EADDA: Towards Novel and Explainable Deep Learning for Early Alzheimer’s Disease Diagnosis Using Autoencoders. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 234–246. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3517

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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