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28 November 2023 Novelty detection technology for Alzheimer's disease based on autoencoder feature extraction and MCD classification
Shuheng Zhang, Yan Zhang, Jie Pan, Xintian Lin, Xuemei Ding, Hongqin Yang
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Proceedings Volume 12772, Real-time Photonic Measurements, Data Management, and Processing VII; 127720H (2023) https://doi.org/10.1117/12.2687619
Event: SPIE/COS Photonics Asia, 2023, Beijing, China
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Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that affects the life quality of millions of people worldwide. To diagnose new cases in a timely manner, we propose a new novelty detection technique that combines Autoencoder and Minimum Covariance Determinant (MCD). The technique consists of two steps: first, we use an Autoencoder to extract low-dimensional and discriminative features from the publicly available ADNI dataset, where we only train the Autoencoder with normal data, making the abnormal data more distinguishable in the feature space; second, based on the features of normal data, we use MCD to construct a decision boundary, and judge the degree of abnormality by the distance of the test point to the boundary. Compared with traditional methods without using Autoencoder, our technique has significant advantages in terms of accuracy and sensitivity, and can effectively deal with data imbalance problem. Experimental results show that our method can efficiently detect novel AD cases, and has a wide range of application prospects.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shuheng Zhang, Yan Zhang, Jie Pan, Xintian Lin, Xuemei Ding, and Hongqin Yang "Novelty detection technology for Alzheimer's disease based on autoencoder feature extraction and MCD classification", Proc. SPIE 12772, Real-time Photonic Measurements, Data Management, and Processing VII, 127720H (28 November 2023); https://doi.org/10.1117/12.2687619
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KEYWORDS
Alzheimer disease
Feature extraction
Alzheimer's disease
Diagnostics
Education and training
Neurological disorders
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