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Cited by 4Dementia and Cognitive Impairment ResearchMedical Image Segmentation TechniquesRetinal Imaging and Analysis

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ConvNeXt-Driven Detection of Alzheimer’s Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes

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Basereh, Mahdiyeh, Abikenari, Matthew Alexander, Sadeghzadeh, Sina, Dunn, Trae, Freichel, René, Siddarth, Prabha, Ghahremani, Dara, Lavretsky, Helen, Buch, Vivek P.·2025·Diagnostics
DOI 10.3390/diagnostics15232997PMC PMC12691013
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Abstract

Background: Alzheimer’s disease (AD) is a leading worldwide cause of cognitive impairment, necessitating accurate, inexpensive diagnostic tools to enable early recognition. Methods: In this study, we present a robust deep learning approach for AD classification based on structural MRI scans, ConvNeXt, an emergent convolutional architecture inspired by vision transformers. We introduce AlzaSet, a clinically curated T1-weighted MRI dataset of 79 subjects (63 with Alzheimer’s disease [AD], 16 cognitively normal controls [NC]) acquired on a 1.5 T Siemens Aera in axial, coronal, and sagittal planes, respectively (12,947 slices in total). Images are neuroradiologist-labeled. Results are reported per plane, with awareness of the class imbalance at the subject level. We further present AlzaSet, a novel, expertly labeled clinical dataset with axial, coronal, and sagittal perspectives from AD and cognitively normal control subjects. Three ConvNeXt sizes (Tiny, Small, Base) were compared and benchmarked against existing state-of-the-art CNN models (VGG16, VGG19, InceptionV3, DenseNet121). Results: ConvNeXt-Base consistently outperformed the other models on coronal slices with an accuracy of 98.37% and an AUC of 0.992. Coronal views were determined to be most diagnostically informative, with emphasis on visualization of the medial temporal lobe. Moreover, comparison with recent ensemble-based techniques showed superior performance with comparable computational efficiency. Conclusions: These results indicate that ConvNeXt-capable models applied to clinically curated datasets have strong potential to provide scalable, real-time AD screening in diverse settings, including both high-resource and resource-constrained settings.

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Metadata

Authors
Basereh, M., Abikenari, M. A., Sadeghzadeh, S., Dunn, T., Freichel, R., Siddarth, P., Ghahremani, D., Lavretsky, H., & Buch, V. P.
Affiliations
  • Allameh Tabataba'i University (IR)
  • Stanford Medicine (US)
  • University of Amsterdam (NL)
  • University of California, Los Angeles (US)
Corresponding author
Abikenari, Matthew Alexander
ORCID
Venue
Diagnostics
Publisher
MDPI AG
Published
2025-11-25
Volume
15
Issue
23
Pages
2997
ISSN
2075-4418 (ISSN-L)
DOI
10.3390/diagnostics15232997
PMC
PMC12691013
OpenAlex
W4416771938
Cited by
4
References
1
Citations by year
2026: 4
FWCI
2.37
Type
journal article
Language
en
Open access
Licence
Locations
  • Diagnostics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • 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
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
Keywords
Alzheimer's disease, ConvNeXt, structural MRI, T1-weighted imaging, medial temporal lobe, deep learning classification, class imbalance, early diagnosis

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