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Diagnostics
Volume 15
Issue 23
10.3390/diagnostics15232997
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Mahdiyeh Basereh
1,†,
Matthew Alexander Abikenari
2,†,
Sina Sadeghzadeh
2,
Trae Dunn
2,
René Freichel
3,
Prabha Siddarth
4,
Dara Ghahremani
4,
Helen Lavretsky
4 and
Vivek P. Buch
2,*
1
Department of Computer Science, Allameh Tabataba’i University, Tehran 1489684511, Iran
2
Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
3
Department of Psychology, University of Amsterdam, 1018 WS Amsterdam, The Netherlands
4
Department of Psychiatry and Biobehavioral Sciences and the Semel Institute for Neuroscience, University of California, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2025, 15(23), 2997; https://doi.org/10.3390/diagnostics15232997
Submission received: 27 September 2025 / Revised: 21 November 2025 / Accepted: 24 November 2025 / Published: 25 November 2025
(This article belongs to the Special Issue AI-Driven Precision Medicine: Innovations in Diagnosis, Prognosis, and Management Response)
Versions Notes

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.
Keywords: Alzheimer’s disease; ConvNeXt; deep learning; structural MRI; coronal plane; computer-aided diagnosis; transfer learning; neuroimaging biomarkers Alzheimer’s disease; ConvNeXt; deep learning; structural MRI; coronal plane; computer-aided diagnosis; transfer learning; neuroimaging biomarkers

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MDPI and ACS Style

Basereh, M.; Abikenari, M.A.; Sadeghzadeh, S.; Dunn, T.; Freichel, R.; Siddarth, P.; Ghahremani, D.; Lavretsky, H.; Buch, V.P. ConvNeXt-Driven Detection of Alzheimer’s Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes. Diagnostics 2025, 15, 2997. https://doi.org/10.3390/diagnostics15232997

AMA Style

Basereh M, Abikenari MA, Sadeghzadeh S, Dunn T, Freichel R, Siddarth P, Ghahremani D, Lavretsky H, Buch VP. ConvNeXt-Driven Detection of Alzheimer’s Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes. Diagnostics. 2025; 15(23):2997. https://doi.org/10.3390/diagnostics15232997

Chicago/Turabian Style

Basereh, Mahdiyeh, Matthew Alexander Abikenari, Sina Sadeghzadeh, Trae Dunn, René Freichel, Prabha Siddarth, Dara Ghahremani, Helen Lavretsky, and Vivek P. Buch. 2025. "ConvNeXt-Driven Detection of Alzheimer’s Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes" Diagnostics 15, no. 23: 2997. https://doi.org/10.3390/diagnostics15232997

APA Style

Basereh, M., Abikenari, M. A., Sadeghzadeh, S., Dunn, T., Freichel, R., Siddarth, P., Ghahremani, D., Lavretsky, H., & Buch, V. P. (2025). ConvNeXt-Driven Detection of Alzheimer’s Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes. Diagnostics, 15(23), 2997. https://doi.org/10.3390/diagnostics15232997

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