{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:51:22Z","timestamp":1792176682541,"version":"4.4.0"},"reference-count":43,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Alzheimer\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/diagnostics15232997","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T16:31:54Z","timestamp":1764088314000},"page":"2997","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ConvNeXt-Driven Detection of Alzheimer\u2019s Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes"],"prefix":"10.3390","volume":"15","author":[{"given":"Mahdiyeh","family":"Basereh","sequence":"first","affiliation":[{"name":"Department of Computer Science, Allameh Tabataba\u2019i University, Tehran 1489684511, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5835-5179","authenticated-orcid":false,"given":"Matthew Alexander","family":"Abikenari","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3360-1016","authenticated-orcid":false,"given":"Sina","family":"Sadeghzadeh","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9855-1932","authenticated-orcid":false,"given":"Trae","family":"Dunn","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ren\u00e9","family":"Freichel","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of Amsterdam, 1018 WS Amsterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prabha","family":"Siddarth","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Biobehavioral Sciences and the Semel Institute for Neuroscience, University of California, Los Angeles, CA 90095, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dara","family":"Ghahremani","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Biobehavioral Sciences and the Semel Institute for Neuroscience, University of California, Los Angeles, CA 90095, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen","family":"Lavretsky","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Biobehavioral Sciences and the Semel Institute for Neuroscience, University of California, Los Angeles, CA 90095, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1151-6128","authenticated-orcid":false,"given":"Vivek P.","family":"Buch","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"key":"ref_1","unstructured":"WHO (2025, November 17). 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