Early Diagnosis of Alzheimer's Disease using Convolutional Neural Network-based MRI
DOI:
https://doi.org/10.11113/mjfas.v19n3.2908Keywords:
Alzheimer’s disease, dementia: MRI image, ADNIAbstract
Alzheimer's disease (AD) is a neurodegenerative ailment that causes cognitive deterioration due to changes in brain structure. Individuals usually see diagnostic symptoms after irreversible brain damage has occurred. In order to slow the course of the illness and enhance the quality of life for AD patients, early diagnosis is crucial. Recent advances in machine learning and scanning have made the use of these methods to detect AD in its earliest stages possible. This article uses deep learning using CNN methods to extract picture characteristics from ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets to improve Alzheimer's disease diagnosis techniques. This descriptor will be used in conjunction with the CNN to categorize the illness and add new characteristics that are more accurate, quicker, and stable than the current features. In this process, an Alzheimer's detection System will be implemented to mitigate the adverse effects of data imbalance on recognition performance, and an integrated multi-depth architectural technology will be introduced to boost recognition quality. Using the suggested model of the convolution neural network (CNN) technique, classification accuracy results were obtained above 97%.
References
[1] Fymat, A. L. (2018). Dementia: A review. J Clin Psychiatr Neurosci, 1(3), 27-34.
Lloret, A., Esteve, D., Lloret, M. A., Cervera-Ferri, A., Lopez, B., Nepomuceno, M., & Monllor, P. (2019). When does Alzheimer′ s disease really start? The role of biomarkers. International Journal of Molecular Sciences, 20(22), 5536.
Jo, T., Nho, K., Risacher, S. L., & Saykin, A. J. (2020). Deep learning detection of informative features in tau PET for Alzheimer’s disease classification. BMC Bioinformatics, 21(21), 1-13.
Sarraf, S., & Tofighi, G. (2016). Classification of alzheimer's disease using fmri data and deep learning convolutional neural networks. arXiv preprint arXiv:1603.08631.
Langa, K. M., & Burke, J. F. (2019). Preclinical Alzheimer disease—early diagnosis or overdiagnosis? JAMA Internal Medicine, 179(9), 1161-1162.
Chételat, G., Arbizu, J., Barthel, H., Garibotto, V., Law, I., Morbelli, S., ... & Drzezga, A. (2020). Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer's disease and other dementias. The Lancet Neurology, 19(11), 951-962.
Salim, A. A., Ghoshal, S. K., & Bakhtiar, H. (2022). Prominent absorption and luminescence characteristics of novel silver-cinnamon core-shell nanoparticles prepared in ethanol using PLAL method. Radiation Physics and Chemistry, 190, 109794.
Al Attar, F., Kadry, S., Manic, K. S., & Meqdad, M. N. (2022, August). Automatic detection of ischemic-stroke-lesion with CNN segmentation: a study. Journal of Physics: Conference Series, 2318(1), 012049. IOP Publishing.
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., ... & Beckett, L. (2005). The Alzheimer's disease neuroimaging initiative. Neuroimaging Clinics, 15(4), 869-877.
Rouast, P. V., Adam, M. T., & Chiong, R. (2019). Deep learning for human affect recognition: Insights and new developments. IEEE Transactions on Affective Computing, 12(2), 524-543.
Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR.
Salim, A. A., Ghoshal, S. K., Danmallam, I. M., Sazali, E. S., Krishnan, G., Aziz, M. S., & Bakhtiar, H. (2021, April). Distinct optical response of colloidal gold-cinnamon nanocomposites: Role of pH sensitization. In Journal of Physics: Conference Series, 1892(1), 012039. IOP Publishing.
McCulloch, W. S., & Pitts, W. (1990). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology, 52, 99-115.
Solares, J. R. A., Raimondi, F. E. D., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., ... & Salimi-Khorshidi, G. (2020). Deep learning for electronic health records: A comparative review of multiple deep neural architectures. Journal of Biomedical Informatics, 101, 103337.
Waheed, S. R., Suaib, N. M., Rahim, M. S. M., Adnan, M. M., & Salim, A. A. (2021, April). Deep Learning Algorithms-based Object Detection and Localization Revisited. Journal of Physics: Conference Series, 1892(1), 012001. IOP Publishing.
Ali, A. H., & Najjar, F. H. (2018, September). Integrating the kernel method to autonomous learning multi-model systems for online data. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-5). IEEE.
Adnan, M. M., Rahim, M. S. M., Al-Jawaheri, K., Ali, M. H., Waheed, S. R., & Radie, A. H. (2020, September). A survey and analysis on image annotation. 2020 3rd International Conference on Engineering Technology and its Applications (IICETA) (pp. 203-208). IEEE.
Waheed, S. R., Rahim, M. S. M., Suaib, N. M., & Salim, A. A. (2023). CNN deep learning-based image to vector depiction. Multimedia Tools and Applications, 1-20.
Zhang, Q., Zhang, M., Chen, T., Sun, Z., Ma, Y., & Yu, B. (2019). Recent advances in convolutional neural network acceleration. Neurocomputing, 323, 37-51.
Sekaran, K., Chandana, P., Krishna, N. M., & Kadry, S. (2020). Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer. Multimedia Tools and Applications, 79(15-16), 10233-10247.
Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24-49.
Tao, J., & Fang, X. (2020). Toward multi-label sentiment analysis: A transfer learning based approach. Journal of Big Data, 7, 1-26.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
Tran, D. T., Iosifidis, A., & Gabbouj, M. (2018). Improving efficiency in convolutional neural networks with multilinear filters. Neural Networks, 105, 328-339.
Magotra, A., & Kim, J. (2020). Improvement of heterogeneous transfer learning efficiency by using hebbian learning principle. Applied Sciences, 10(16), 5631.
Valencia, D., Fard, S. F., & Alimohammad, A. (2020). An artificial neural network processor with a custom instruction set architecture for embedded applications. IEEE Transactions on Circuits and Systems I: Regular Papers, 67(12), 5200-5210.
Abbas, A. M., Abid, M. A., Abbas, K. N., Aziz, W. J., & Salim, A. A. (2021, April). Photocatalytic activity of Ag-ZnO nanocomposites integrated essential ginger oil fabricated by green synthesis method. Journal of Physics: Conference Series, 1892(1), 012005. IOP Publishing.
Ramzan, F., Khan, M. U. G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A., & Mehmood, Z. (2020). A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks. Journal of Medical Systems, 44, 1-16.
Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021). Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific Reports, 11(1), 3254.
Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., ... & Hamarneh, G. (2017). BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 146, 1038-1049.
Salim, A. A., Bakhtiar, H., Shamsudin, M. S., Aziz, M. S., Johari, A. R., & Ghoshal, S. K. (2022). Performance evaluation of rose bengal dye-decorated plasmonic gold nanoparticles-coated fiber-optic humidity sensor: A mechanism for improved sensing. Sensors and Actuators A: Physical, 347, 113943.
Hathot, S. F., Abbas, S. I., AlOgaili, H. A. T., & Salim, A. A. (2022). Influence of deposition time on absorption and electrical characteristics of ZnS thin films. Optik, 260, 169056.
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Copyright (c) 2023 MUHAMMAD SAFWAN ABD AZIZ, Karrar A Kadhim, Farhan Mohamed, Ammar AbdRaba Sakran, Myasar Mundher Adnan, Ghalib Ahmed Salman
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