Abstract
Alzheimer's Disease (AD) is a progressive, permanent and irreversible neurological disorder of the brain that causes brain atrophy, death of brain cells, destruction of memory skills, and deterioration of thinking and social interactions. It has become a serious disease all over the world. Although there is no cure to reverse the progression of Alzheimer's disease, early detection of Alzheimer's disease would be very effective in the medical field and it could help for the treatment. This paper focuses on the early detection of stages of cognitive impairment and Alzheimer's disease using neuroimaging with transformative Learning (TL). Magnetic Resonance Imaging (MRI) images obtained from the Alzheimer's Disease Neuroimaging Database (OASIS) are categorized using a TL approach. Our proposed pre-trained networks such as InceptionV3-M and VGG16-M are modified applying batch normalization and regularization. The classification performance of these two networks is analyzed with the help of confusion matrix and its parameters. Simulation results have shown that the VGG16 model gives 97.06% accuracy. It also observed that the proposed system gives more accurate results than any previous studies performed previously on the OASIS dataset. These findings could help drive the development of computer-aided diagnosis.
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References
Francolini, G., et al.: Artificial intelligence in radiotherapy: state of the art and future directions. Med. Oncol. 37(6), 1–9 (2020)
Kwak, K., Niethammer, M., Giovanello, K.S., Styner, M., Dayan, E.: Differential role for hippocampal subfields in Alzheimer’s disease progression revealed with deep learning. Cereb. Cortex 32(3), 467–478 (2022)
Mathers, C.D., et al.: Global Burden of Disease 2000: Version 2 methods and results, In the Journal of Global Programme on Evidence for Health Policy Discussion, vol. 50. World Health Organization (2002)
Venugopalan, J., Tong, L., Hassanzadeh, H.R., Wang, M.D.: Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci. Rep. 11(1), 1–13 (2021)
Fang, X., Liu, Z., Xu, M.: Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer’s disease diagnosis. IET Image Proc. 14(2), 318–326 (2020)
Lee, R., Choi, H., Park, K.-Y., Kim, J.-M., Seok, J.W.: Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach. Eur. J. Nucl. Med. Mol. Imaging 49(4), 1254–1262 (2022)
Tyrrell, D.A.J., Parry, R.P., Crow, T.J., Johnstone, E., Ferrier, I.N.: Possible virus in schizophrenia and some neurological disorders. The Lancet 313(8121), 839–841 (1979)
Clarke, L.P., et al.: MRI segmentation: methods and applications. Magn. Reson. Imaging 13(3), 343–368 (1995)
Sharma, S., Mandal, P.K.: A comprehensive report on machine learning-based early detection of Alzheimer’s disease using multi-modal neuroimaging data. ACM Comput. Surv. 55(2), 1–44 (2023)
Basheera, S., Sai Ram, M.S.: Convolution neural network–based Alzheimer’s disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 5(1), 974–986 (2019)
Saikumar, K., Rajesh, V., Babu, B.S.: Heart disease detection based on feature fusion technique with augmented classification using deep learning technology. Traitement du Signal 39(1), 31–42 (2022)
Zeitzer, J.M., et al.: Phenotyping apathy in individuals with Alzheimer disease using functional principal component analysis. Am. J. Geriatr. Psychiatry 21(4), 391–397 (2013)
Lindberg, O., et al.: Hippocampal shape analysis in Alzheimer’s disease and frontotemporal lobar degeneration subtypes. Journal of Alzheimer’s Disease 30(2), 355–365 (2012)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, vol. 37, pp. 448–456 (2015)
Higami, Y., Yamakawa, M., Shigenobu, K., Kamide, K., Makimoto, K.: High frequency of getting out of bed in patients with Alzheimer’s disease monitored by non-wearable actigraphy. Geriatrics & Gerontology International 19(2), 130–134 (Feb. 2019)
Ebrahimighahnavieh, M.A., Luo, S., Chiong, R.: Deep learning to detect Alzheimer’s disease from neuroimaging: a systematic literature review. Comput. Methods Programs Biomed. 187, 1–48 (2020)
Odusami, M., et al.: Comparable Study of Pre-trained Model on Alzheimer Disease Classification. In: Gervasi, O., et al. (ed.) Computational Science and Its Applications – ICCSA 2021. In the ICCSA 2021, Lecture Notes in Computer Science, vol. 12953 (2021)
Maqsood, M., et al.: Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans. The Journal of Sensors 19, 2645 (2019)
Islam, J., Zhang, Y.: A novel deep learning based multi-class classification method for alzheimer’s disease detection using brain MRI data. In: the Proc. International Conference on Brain Informatics, pp. 213–222 (2017)
Wang, S., et al.: Classification of alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. The Journal of Med Syst 42(85) (2018)
Khagi, B., et al.: CNN Models Performance Analysis on MRI images of OASIS dataset for distinction between Healthy (i.e. Non Demented) and Alzheimer’s patient (i.e. Demented). In: the 2019 International Conference on Electronics Information and Communication (ICEIC), pp. 1–4 (2019)
Khagi, B., et al.: Models Performance Analysis on MRI images of OASIS dataset for distinction between Healthy and Alzheimer’s patient. In: the 2019 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1–4 (2019)
Tsafas, V., et al.: Application of a deep-learning technique to non-linear images from human tissue biopsies for shedding new light on breast cancer diagnosis. IEEE J. Biomed. Health Inform. 26(3), 1188–1195 (2022)
Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015)
Lee, H., Song, J.: Introduction to convolutional neural network using Keras; an understanding from a statistician, Communications for Statistical Applications and Methods, vol. 26, no. 6. Communications for Statistical Applications and Methods, pp. 591–610 ( 30-Nov 2019)
Xia, Z., et al.: A novel end-to-end hybrid network for Alzheimer’s disease detection using 3D CNN and 3D CLSTM. In: the IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1–4. IEEE (2020)
Yang, H., et al.: Study of brain morphology change in Alzheimer’s disease and amnestic mild cognitive impairment compared with normal controls. In: the General Psychiatry, vol. 32 (2019)
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Guesmi, D., Salah, F., Ayed, Y.B. (2023). Recognition of Alzheimer’s Disease Based on Transfer Learning Approach Using Brain MR Images with Regularization. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_12
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