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Volume 13
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10.3390/brainsci13020306
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Article

Dual Semi-Supervised Learning for Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Neuropsychological Data

by
Yan Wang
1,
Xuming Gu
1,
Wenju Hou
1,
Meng Zhao
2,
Li Sun
2 and
Chunjie Guo
3,*
1
Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun 130021, China
3
Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
*
Author to whom correspondence should be addressed.
Brain Sci. 2023, 13(2), 306; https://doi.org/10.3390/brainsci13020306
Submission received: 4 December 2022 / Revised: 27 January 2023 / Accepted: 7 February 2023 / Published: 10 February 2023
(This article belongs to the Topic Translational Advances in Neurodegenerative Dementias)
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Abstract

Deep learning has shown impressive diagnostic abilities in Alzheimer’s disease (AD) research in recent years. However, although neuropsychological tests play a crucial role in screening AD and mild cognitive impairment (MCI), there is still a lack of deep learning algorithms only using such basic diagnostic methods. This paper proposes a novel semi-supervised method using neuropsychological test scores and scarce labeled data, which introduces difference regularization and consistency regularization with pseudo-labeling. A total of 188 AD, 402 MCI, and 229 normal controls (NC) were enrolled in the study from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We first chose the 15 features most associated with the diagnostic outcome by feature selection among the seven neuropsychological tests. Next, we proposed a dual semi-supervised learning (DSSL) framework that uses two encoders to learn two different feature vectors. The diagnosed 60 and 120 subjects were randomly selected as training labels for the model. The experimental results show that DSSL achieves the best accuracy and stability in classifying AD, MCI, and NC (85.47% accuracy for 60 labels and 88.40% accuracy for 120 labels) compared to other semi-supervised methods. DSSL is an excellent semi-supervised method to provide clinical insight for physicians to diagnose AD and MCI.
Keywords: Alzheimer’s disease; semi-supervised Learning; neuropsychological test; difference regularization Alzheimer’s disease; semi-supervised Learning; neuropsychological test; difference regularization

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MDPI and ACS Style

Wang, Y.; Gu, X.; Hou, W.; Zhao, M.; Sun, L.; Guo, C. Dual Semi-Supervised Learning for Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Neuropsychological Data. Brain Sci. 2023, 13, 306. https://doi.org/10.3390/brainsci13020306

AMA Style

Wang Y, Gu X, Hou W, Zhao M, Sun L, Guo C. Dual Semi-Supervised Learning for Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Neuropsychological Data. Brain Sciences. 2023; 13(2):306. https://doi.org/10.3390/brainsci13020306

Chicago/Turabian Style

Wang, Yan, Xuming Gu, Wenju Hou, Meng Zhao, Li Sun, and Chunjie Guo. 2023. "Dual Semi-Supervised Learning for Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Neuropsychological Data" Brain Sciences 13, no. 2: 306. https://doi.org/10.3390/brainsci13020306

APA Style

Wang, Y., Gu, X., Hou, W., Zhao, M., Sun, L., & Guo, C. (2023). Dual Semi-Supervised Learning for Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Neuropsychological Data. Brain Sciences, 13(2), 306. https://doi.org/10.3390/brainsci13020306

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