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U-net based analysis of MRI for Alzheimer’s disease diagnosis

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Abstract

Alzheimer’s disease (AD) is the most common type of dementia that still has no effective treatment. Accurate classification of AD can help in its diagnosis and selection of the most effective treatment options. In the last decade, several studies have proven the effectiveness of deep learning algorithms for AD diagnosis. In this paper, we propose a U-net style model for AD diagnosis using 3D T1-weighted magnetic resonance images (MRI). Combining with deep supervision has been proved to be effective in improving the performance of the model. Our method has been tested on a subset of ADNI dataset and AIBL dataset and achieves a superior average accuracy of \(95.71\pm 1.36\%\) for AD versus NC (normal control), \(90.14\pm 3.66\%\) for EMCI (early mild cognitive impairment) versus LMCI (late mild cognitive impairment), \(90.05\pm 2.63\%\) for AD versus LMCI, and \(87.98\pm 4.54\%\) for NC versus EMCI, respectively. Besides these binary-classification tasks, we also test this model for multi-class classification task (AD vs. NC vs. EMCI vs. LMCI) and it achieves an accuracy of \(86.47\pm 9.60\%\). Furthermore, 3D-Grad-CAM method is used to visualize the focused areas of the proposed model. We find that the proposed model pays more attention to the characteristics of the ventricles, hippocampus, and some regions of cortex, which have been proven to be affected by AD.

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Acknowledgements

This work is supported by the National Key R&D Program of China under Grant (2019YFB1311600), Shanghai Science and Technology Committee (STCSM) No 17411953500, 1841195210, and 17511104202. The authors would like to thank the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) for providing data for this paper.

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  1. Johann Li Contributed equally as first author.

Authors and Affiliations

  1. The School of Computer Science and Technology, Xidian University, Shaanxi, China

    Zhonghao Fan, Johann Li, Liang Zhang, Guangming Zhu & Peiyi Shen

  2. Data and Virtual Research Room, Shanghai Broadband Network Center, Shanghai, China

    Ping Li & Xiaoyuan Lu

  3. The Discipline of IT, Mathematics and Statistics, Murdoch University, Perth, Australia

    Syed Afaq Ali Shah

  4. The School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia

    Mohammed Bennamoun

  5. Pet Center, Huashan Hospital, Fudan University, Shanghai, China

    Tao Hua

  6. School of Computer Science and Engineering, Xi’an University of Technology, Shaanxi Key Laboratory for Network Computing and Security Technology, Shaanxi, China

    Wei Wei

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  1. Zhonghao Fan
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Fan, Z., Li, J., Zhang, L. et al. U-net based analysis of MRI for Alzheimer’s disease diagnosis. Neural Comput & Applic 33, 13587–13599 (2021). https://doi.org/10.1007/s00521-021-05983-y

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