Multi-classification for Predicting Alzheimer's Disease Using 1.5T1-weighted MRI Imaging and Deep Learning 2D CNN

Shahad Shahad Haitham Ali

doi:10.47134/jtsi.v2i4.4987

Authors

Shahad Haitham Ali Middle Technical University

DOI:

https://doi.org/10.47134/jtsi.v2i4.4987

Keywords:

Magnetic Resonance Imaging, Multi-classification, 2D-CNN, Image Enhancement Algorithms, Alzheimer's Disease, Algorithm Deep Learning, AD, MCI, Predicting

Abstract

Medical imaging holds the pivotal role in clinical diagnosis, education, research work, and treatment of medicine. Medical professionals are tasked with analyzing and interpreting high-level medical data, which is extremely difficult in nature due to the intricate nature of medical images. Deep learning techniques are emerging as strong tools, yielding promising and correct results in the analysis of medical data. Alzheimer's disease, which affects one in every ten individuals aged 65 and above, is a central focus where such advancements are aimed. Artificial intelligence has proved capable of distinguishing between healthy brains and Alzheimer's disease-affected brains. The etiology of the disease lies in abnormal proteins accumulating inside and outside neuronal cells, causing irreparable loss of memory. Alzheimer's disease is the most prevalent form of dementia, and "Mild Cognitive Impairment" (MCI) typically presents as an early indicator, identifying patients who are at higher risk of Alzheimer's disease. However, not all MCI patients go on to develop Alzheimer's, and this highlights the importance of effective interventions. While some patients with MCI (MCI-nc) remain stable, others will progress to Alzheimer's disease. Here, a CNN model was designed and trained first with four classifiers and later retrained with five classifiers. The accuracy rate of the four-classifier model was 98%, while that of the five-classifier model was slightly higher with an accuracy of 98.67%.

References

Abed, M. T., Nabil, S. A., & Fatema, U. (2019). Early prediction of Alzheimer's disease using convolutional neural network. Brac University.

Agarwal, D., Berbís, M. Á., Luna, A., Lipari, V., Ballester, J. B., & de la Torre-Díez, I. (2023). Automated medical diagnosis of Alzheimer’s disease using an efficient net convolutional neural network. Journal of Medical Systems, 47(1), 57. https://doi.org/10.1007/s10916-023-01913-9

Alzheimer, A. (1907). Ueber eine eigenartige Erkrankung der Hirnrinde. Allgemeine Zeitschrift für Psychiatrie und Psychisch-Gerichtliche Medizin, 64, 146–148.

Alzheimer, A., Stelzmann, R. A., Schnitzlein, H. N., & Murtagh, F. R. (1995). An English translation of Alzheimer’s 1907 paper: “Über eine eigenartige Erkankung der Hirnrinde.” Clinical Anatomy, 8(6), 429–431. https://doi.org/10.1002/ca.980080612

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 53. https://doi.org/10.1186/s40537-021-00444-8

Amezquita-Sanchez, J. P., Adeli, A., & Adeli, H. (2016). A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG). Behavioural Brain Research, 305, 174–180. https://doi.org/10.1016/j.bbr.2016.03.002

Amezquita-Sanchez, J. P., Mammone, N., Morabito, F. C., Marino, S., & Adeli, H. (2019). A novel methodology for automated differential diagnosis of mild cognitive impairment and Alzheimer’s disease using EEG signals. Journal of Neuroscience Methods, 322, 88–95. https://doi.org/10.1016/j.jneumeth.2019.05.007

Bloom, G. S. (2014). Amyloid-β and tau: The trigger and bullet in Alzheimer disease pathogenesis. JAMA Neurology, 71(4), 505–508. https://doi.org/10.1001/jamaneurol.2013.5847

Desai, B., Kushwaha, U., Jha, S., & NMIMS, M. (2020). Image filtering—Techniques, algorithms, and applications. Applied GIS, 7(2), 970–975.

Ebrahimighahnavieh, M. A., Luo, S., & Chiong, R. (2020). Deep learning to detect Alzheimer’s disease from neuroimaging: A systematic literature review. Computer Methods and Programs in Biomedicine, 187, 105242. https://doi.org/10.1016/j.cmpb.2019.105242

Fang, C., Li, C., Cabrerizo, M., Barreto, A., Andrian, J., Rishe, N., Loewenstein, D., Duara, R., & Adjouadi, M. (2018). Gaussian discriminant analysis for optimal delineation of mild cognitive impairment in Alzheimer’s disease. International Journal of Neural Systems, 28(4), 1850017. https://doi.org/10.1142/S0129065718500172

Feng, W., Halm-Lutterodt, N. V., Tang, H., Mecum, A., Mesregah, M. K., Ma, Y., Li, H., Zhang, F., Wu, Z., & Yao, E. (2020). Automated MRI-based deep learning model for detection of Alzheimer’s disease process. International Journal of Neural Systems, 30(5), 2050032. https://doi.org/10.1142/S0129065720500323

Gunturk, B., & Li, X. (2018). Image restoration. CRC Press.

Jain, R., Jain, N., Aggarwal, A., & Hemanth, D. J. (2019). Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cognitive Systems Research, 57, 147–159. https://doi.org/10.1016/j.cogsys.2018.12.015

Jensen, J. R., & Lulla, K. (1987). Introductory digital image processing: A remote sensing perspective. Prentice Hall.

Kaur, S., Gupta, S., Singh, S., & Gupta, I. (2022). Detection of Alzheimer’s disease using deep convolutional neural network. International Journal of Image and Graphics, 22(1), 2140012. https://doi.org/10.1142/S0219467821400127

Koepsell, T. D., & Monsell, S. E. (2012). Reversion from mild cognitive impairment to normal or near-normal cognition: Risk factors and prognosis. Neurology, 79(15), 1591–1598. https://doi.org/10.1212/WNL.0b013e31826e25b4

Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827

Menikdiwela, M., Nguyen, C., & Shaw, M. (2018). Deep learning on brain cortical thickness data for disease classification. In 2018 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–5). IEEE. https://doi.org/10.1109/DICTA.2018.8615763

Michelucci, U. (2018). Applied deep learning: A case-based approach to understanding deep neural networks. Apress. https://doi.org/10.1007/978-3-319-73004-2

Muniyappan, S., Allirani, A., & Saraswathi, S. (2013). A novel approach for image enhancement by using contrast limited adaptive histogram equalization method. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1–6). IEEE. https://doi.org/10.1109/ICCCNT.2013.6726614

Nawaz, A., Anwar, S. M., Liaqat, R., Iqbal, J., Bagci, U., & Majid, M. (2020). Deep convolutional neural network based classification of Alzheimer’s disease using MRI data. In 2020 IEEE 23rd International Multitopic Conference (INMIC) (pp. 1–6). IEEE. https://doi.org/10.1109/INMIC50486.2020.9318135

Ocasio, E., & Duong, T. Q. (2021). Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI. PeerJ Computer Science, 7, e560. https://doi.org/10.7717/peerj-cs.560

Ongsulee, P. (2017). Artificial intelligence, machine learning and deep learning. In 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE) (pp. 1–6). IEEE. https://doi.org/10.1109/ICTKE.2017.8259629

Owotogbe, J., Ibiyemi, T., & Adu, B. (2019). A comprehensive review on various types of noise in image processing. International Journal of Scientific & Engineering Research, 10(7), 388–393.

Padzil, F. M., & Crebbin, D. G. (2016). Linear and nonlinear filter for image processing using MATLAB’s image processing toolbox. Murdoch University.

Salem, N., Malik, H., & Shams, A. (2019). Medical image enhancement based on histogram algorithms. Procedia Computer Science, 163, 300–311. https://doi.org/10.1016/j.procs.2019.12.103

Sarvamangala, D., & Kulkarni, R. V. (2022). Convolutional neural networks in medical image understanding: A survey. Evolutionary Intelligence, 15(1), 1–22. https://doi.org/10.1007/s12065-020-00540-3

Schwenk, K., & Huber, F. (2015). Connected component labeling algorithm for very complex and high-resolution images on an FPGA platform. In High-Performance Computing in Remote Sensing V (pp. 9–22). SPIE. https://doi.org/10.1117/12.2189062

Yang, C., Sun, X., Tao, W., Li, X., Zhang, J., Jia, J., Chen, K., & Zhang, Z. (2017). Multistage grading of amnestic mild cognitive impairment: The associated brain gray matter volume and cognitive behavior characterization. Frontiers in Aging Neuroscience, 8, 332. https://doi.org/10.3389/fnagi.2016.00332

Yousefi, J. (2011). Image binarization using Otsu thresholding algorithm. University of Guelph, 10.

Zhang, Y., Liu, S., & Yu, X. (2020). Longitudinal structural MRI analysis and classification in Alzheimer’s disease and mild cognitive impairment. International Journal of Imaging Systems and Technology, 30(2), 421–433. https://doi.org/10.1002/ima.22402