Abstract
Alzheimer’s disease is a classic form of dementia that is progressive and irreversible. It is crucial to use 3D magnetic resonance imaging (MRI) to diagnose Alzheimer’s disease (AD) in the early treatment, which is beneficial for controlling the disease and allows the patient to receive a proper cure. Previous studies use a deeper model with more parameters and a tedious training process. These methods neglect the local changes in brain regions, and their performance is unsatisfactory. Therefore, in this paper, we propose a 3D Attention Residual Network (3D ARNet) to classify 3D brain MRI into 4-way classification. Specifically, our proposed 3D ARNet is a shallow network with only 10 layers, which is compact and converges fast. Moreover, we propose the attention mechanism to utilize the expressive information in brain MRI and apply the Instance-Batch Normalization (IBN) to highlight the global changes and local changes in MRI at the same time. We conduct extensive experiments on benchmark datasets (i.e., ADNI dataset). The experimental results demonstrate that our method is more efficient in diagnosing Alzheimer’s disease.
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Acknowledgement
This work was supported in part by the Sichuan Science and Technology Program under Grant 2020YFS0307, Mianyang Science and Technology Program 2020YFZJ016, SWUST Doctoral Foundation under Grant 19zx7102. *Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). For up-to-date information, see http://adni-info.org/.
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Liu, M., Tang, J., Yu, W., Jiang, N. (2021). Attention-Based 3D ResNet for Detection of Alzheimer’s Disease Process. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_28
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