Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model
Open accessAbstract
In this study, the anomaly analysis of Alzheimer’s disease using positron emission tomography (PET) images using an unsupervised proposed adversarial model is investigated. The model consists of three parts: a parallel-network encoder, which is comprised of a convolutional pipeline and a dilated convolutional pipeline that extracts global and local features and concatenates them, a decoder that reconstructs the input image from the obtained feature vector, and a discriminator that distinguishes if the input image image is real or fake. The hypothesis is that if the proposed model is trained with only normal brain images, the corresponding construction loss for normal images should be minimal. However, if the input image belongs to a class that is designated as an anomaly that which the model is not trained with, then the construction loss will be high. This will reflect during the anomaly score comparison between the normal and the anomalous image. A multi-case analysis is performed for three major classes using the Alzheimer’s Disease Neuroimaging Initiative dataset, Alzheimer’s disease, mild cognitive impairment, and normal control. The base parallel-encoder network shows better classification accuracy than the benchmark models, and the proposed model that is built on the parallel model outperforms the benchmark anomaly detection models. The proposed model gave out 96.03% and 75.21% in classification and area under the curve score, respectively. Additionally, a qualitative evaluation done by using Fréchet inception distance gave a better score than the state-of-the-art by three points.
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Baydargil et al.'s unsupervised adversarial parallel model for AD anomaly analysis exhibits classification accuracy of 96.03% and AUC of 75.21% in distinguishing AD, MCI, and normal control groups.
“The model exhibits robust classification with rates and area under the curve (AUC) scores reaching 96.03% and 75.21%, respectively, underscoring its effective discriminative performance.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [71]
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Self-citation
- Authors
- Baydargil, H. B., Park, J.-S., & Kang, D.-Y.
- Affiliations
- Kyungsung University (KR)
- Dong-A University (KR)
- Corresponding author
- Park, Jang-Sik
- Kang, Do-Young
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2021-03-02
- Volume
- 11
- Issue
- 5
- Pages
- 2187
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3133532155
- Cited by
- 30
- References
- 63
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 3 · 2021: 3
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Korean Ministry of Science, ICT & Future Planning (NRF-2018R1A2B2008178)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- Alzheimer's disease, PET images, anomaly detection, generative adversarial networks, parallel encoder, dilated convolution, mild cognitive impairment, ADNI dataset, Fréchet Inception Distance, brain image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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