Alzheimer's Disease Classification System of Brain Magnetic Resonance Images based on light weight U-Net Network
Keywords:
dementia, deep neural network (DNN), medical image processing, Alzheimer’s disease (AD), brain imagingAbstract
Alzheimer’s Disease (AD) is a neurodegenerative disease that commonly occurs in older people. Recently, researchers created a novel approach based on deep learning, a branch of machine learning, for the instinctive analysis of AD. It is characterized by both cognitive and functional impairment. However, as AD has an unclear pathological cause, it can be hard to diagnose with confidence. This is even more so in the early stage of Mild Cognitive Impairment (MCI). Accurate and rapid classification of AD is critical for the diagnosis and treatment of elder patients. However, MRI images often present challenges such as variable size and shape, low contrast, blurred boundaries, and numerous shadows. To address these issues, In this research article, we propose a lightweight U-Net architecture(LW-Unet) for the classification of AD. We add residual blocks, and residual convolutional layer pathways are integrated into the atrous spatial pyramid pooling (ASPP) module and Multi-Scale Context Fusion Block (MSCFB). To fuse convolutional feature maps in encoding layers, the ASPP unit used a learnable set of parameters. An efficient architecture for feature extraction during the encoding step is the ASPP unit. We integrated the AD unit with the benefits of the U-Net network for deep and shallow features. A mixed loss function composed of Dice loss, Bce loss, and Focal loss functions is used. The experimental results are validated using the Sensitivity, PPV, Dice similarity coefficient(DSC), and IOU values. The AD classification accuracy of the proposed method LW-Unet is 98.40, and 98.91 in the ADNI and NACC Data sets respectively. The results show a good performance of the proposed MLCNN model in terms of dice similarity coefficient criteria and IOU value.
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Cheng, J., Liu, Z., Guan, H., Wu, Z., Zhu, H., Jiang, J., Wen, W., Tao, D., Liu, T., 2021. Brain age estimation from mri using cascade networks with ranking loss. IEEE Transactions on Medical Imaging 40, 3400–3412.
Gorji T. H., Naima K. A deep learning approach for diagnosis of mild cognitive impairment based on MRI images. Brain Sciences. 2019;9(9):p. 217.
Guévremont, D.; Tsui, H.; Knight, R.; Fowler, C.J.; Masters, C.L.; Martins, R.N.; Abraham, W.C.; Tate, W.P.; Cutfield, N.; Williams, J.M. Plasma microRNA vary in association with the progression of Alzheimer’s disease. Alzheimer’s Dement. 2022, 14, e12251.
Gupta Y., Lee K. H., Choi K. Y., Lee J. J., Kim B. C., Kwon G.-R. Alzheimer’s disease diagnosis based on cortical and subcortical features. Journal of Healthcare Engineering. 2019;2019:13.
Ho, N.H., Yang, H.J., Kim, J., Dao, D.P., Park, H.R., Pant, S., 2022. Predicting progression of alzheimer’s disease using forward-to-backward bidirectional network with integrative imputation. Neural Networks 150,422–439.
Janousova, E.; Vounou, M.; Wolz, R.; Gray, K.R.; Rueckert, D.; Montana, G. Biomarker discovery for sparse classification of brain images in Alzheimer’s disease. Ann. BMVA 2012, 2, 1–11.
Khojaste-Sarakhsi, M., Haghighi, S.S., Ghomi, S.F., Marchiori, E., 2022. Deep learning for alzheimer’s disease diagnosis: A survey. Artificial Intelligence in Medicine , 102332.
Klöppel, S.; Stonnington, C.M.; Chu, C.; Draganski, B.; Scahill, R.I.; Rohrer, J.D.; Fox, N.C.; Jack, C.R., Jr.; Ashburner, J.; Frackowiak, R.S. Automatic classification of MR scans in Alzheimer’s disease. Brain 2008, 131, 681–689.
A.J. Prabhu and A. Jayachandran, Mixture model segmentation system for parasagittal meningioma brain tumor classification based on hybrid feature vector, Journal of Medical System 42(12) (2018)..
Li, W., Lin, X., Chen, X., 2020. Detecting alzheimer’s disease based on 4d fmri: An exploration under deep learning framework. Neurocomputing 388, 280–287.
Liu, C., Huang, F., Qiu, A., Initiative, A.D.N., et al., 2023. Monte carlo ensemble neural network for the diagnosis of alzheimer’s disease. Neural Networks 159, 14–24.
Liu, S.; Song, Y.; Cai, W.; Pujol, S.; Kikinis, R.; Wang, X.; Feng, D. Multifold Bayesian kernelization in Alzheimer’s diagnosis. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Nagoya, Japan, 22–26 September 2013; Springer: Berlin/Heidelberg, Germany, 2013.
Michaud, T.L., Su, D., Siahpush, M., Murman, D.L., 2017. The risk of incident mild cognitive impairment and progression to dementia considering mild cognitive impairment subtypes. Dementia and geriatric cognitive disorders extra 7, 15–29.
Nozadi S. H., Kadoury S. Classification of Alzheimer’s and MCI patients from semantically parcelled PET images: a comparison between AV45 and FDG-PET. International Journal of Biomedical Imaging. 2018;2018:13.
Pan, X., Phan, T.L., Adel, M., Fossati, C., Gaidon, T., Wojak, J., Guedj, E., 2020. Multi-view separable pyramid network for ad prediction at mci stage by 18 f-fdg brain pet imaging. IEEE Transactions on Medical Imaging 40, 81–92.
Park, J.H., Cho, H.E., Kim, J.H., Wall, M.M., Stern, Y., Lim, H., Yoo, S., Kim, H.S., Cha, J., 2020. Machine learning prediction of incidence of alzheimer’s disease using large-scale administrative health data. NPJ digital medicine 3, 46.
Parmar, H., Nutter, B., Long, R., Antani, S., Mitra, S., 2020a. Spatiotemporal feature extraction and classification of alzheimer’s disease using deep learning 3d-cnn for fmri data. Journal of Medical Imaging 7, 056001.
Parmar, H.S., Nutter, B., Long, R., Antani, S., Mitra, S., 2020b. Deep learning of volumetric 3d cnn for fmri in alzheimer’s disease classification, in: Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, SPIE. pp. 66–71.
A. Jayachandran and R. Dhanasekaran, Automatic detection of brain tumor in magnetic resonance images using multi texton histogram and support vector machine, International Journal of Imaging Systems and Technology 23(2) (2013), 97–103..
Qiu, A.; Fennema-Notestine, C.; Dale, A.M.; Miller, M.I. Regional shape abnormalities in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2009, 45, 656–661.
Risacher, S.; Saykin, A.; Wes, J.; Shen, L.; Firpi, H.; McDonald, B. Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort. Curr. Alzheimer Res. 2009, 6, 347–361.
Ronneberger O, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervent., (2015) 234-241.
Tulsani A, P. Kumar, S. Pathan, Automated segmentation of optic disc and optic cup for glaucoma assessment using improved unet++ architecture, Biocybern Biomed Eng 41 (18),2021.
Wang, C., Li, Y., Tsuboshita, Y., Sakurai, T., Goto, T., Yamaguchi, H.,Yamashita, Y., Sekiguchi, A., Tachimori, H., Initiative, A.D.N., 2022. A high-generalizability machine learning framework for predicting the progression of alzheimer’s disease using limited data. NPJ digital medicine 5, 43.
Xia, Z., Yue, G., Xu, Y., Feng, C., Yang, M., Wang, T., Lei, B., 2020. A novel end-to-end hybrid network for alzheimer’s disease detection using 3d cnn and 3d clstm, in: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 1–4.
Yang Z, X. Peng, and Z. Yin, “Deeplab v3 plus-net for image semantic segmentation with channel compression,” in Proceedings of IEEE 20th International Conference on Communication Technology (ICCT). IEEE, 2020, pp. 1320–1324.
Zhang, D.; Wang, Y.; Zhou, L.; Yuan, H.; Shen, D. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 2011, 55, 856–867.
Zhang, F., Li, Z., Zhang, B., Du, H., Wang, B., Zhang, X., 2019. Multimodal deep learning model for auxiliary diagnosis of alzheimer’s disease. Neurocomputing 361, 185–195
Zhang, Y.; Dong, Z.; Phillips, P.; Wang, S.; Ji, G.; Yang, J.; Yuan, T.-F. Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci. 2015, 9, 66.
Zhu,W., Sun, L., Huang, J., Han, L., Zhang, D., 2021. Dual attention multiinstance deep learning for alzheimer’s disease diagnosis with structural mri. IEEE Transactions on Medical Imaging 40, 2354–2366.
A. Jayachandran and R. Dhanasekaran, Brain tumor detection using fuzzy support vector machine classification based on a texton co-occurrence matrix, Journal of imaging Science and Technology 57(1) (2013), 10507-1–10507-7(7).
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