{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T17:14:01Z","timestamp":1772558041185,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"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 is the most common type of dementia and a progressive neurodegenerative disease that begins with neuronal damage and leads to a reduction in brain tissue. Currently, there is no cure for this disease, and existing approaches focus on alleviating symptoms. Methods: This study proposes NeuroFusion-ViT, a highly accurate and computationally efficient hybrid deep learning model for early-stage detection of Alzheimer\u2019s disease. The model combines an EVA-02-based Vision Transformer (ViT) with the ConvNeXt-Small CNN architecture, providing powerful representation learning that can process both global context and local details. The proposed Gated Cross-Attention Fusion (G-CAF) mechanism dynamically combines two different features, offering high discriminative power and model stability. Results: In experiments conducted on the OASIS MRI dataset, the model achieved 99.86% accuracy, 0.9989 Macro F1, and 0.999 ROC-AUC values, demonstrating clear superiority over single-modal and hybrid models described in the literature. Furthermore, 5-fold cross-validation results also support the model\u2019s high generalizability. Ablation studies showed that each of the components\u2014cross-attention, gate mechanism, Dual LayerNorm, and FFN-Dropout\u2014made a meaningful contribution to performance. Conclusions: The results demonstrate that the NeuroFusion-ViT architecture offers a reliable, stable, and clinically applicable solution for Alzheimer\u2019s stage classification.<\/jats:p>","DOI":"10.3390\/diagnostics16050754","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:22:21Z","timestamp":1772551341000},"page":"754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NeuroFusion-ViT: A Hybrid CNN\u2013EVA Transformer Model with Cross-Attention Fusion for MRI-Based Alzheimer\u2019s Stage Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1685-7802","authenticated-orcid":false,"given":"Derya","family":"\u00d6zt\u00fcrk S\u00f6ylemez","sequence":"first","affiliation":[{"name":"Vocational School of Health Services, Sinop University, Sinop 57000, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6309-0889","authenticated-orcid":false,"given":"Sevin\u00e7","family":"Ay Do\u011fru","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Firat University, Elazig 23000, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Muksimova, S., Umirzakova, S., Baltayev, J., and Cho, Y.I. (2025). Multi-Modal Fusion and Longitudinal Analysis for Alzheimer\u2019s Disease Classification Using Deep Learning. Diagnostics, 15.","DOI":"10.3390\/diagnostics15060717"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Slimi, H., Balti, A., Abid, S., and Sayadi, M. (2024). A Combinatorial Deep Learning Method for Alzheimer\u2019s Disease Classification-Based Merging Pre224ed Networks. Front. Comput. Neurosci., 18.","DOI":"10.3389\/fncom.2024.1444019"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Han, L. (2025). AD-Diff: Enhancing Alzheimer\u2019s Disease Prediction Accuracy through Multimodal Fusion. Front. Comput. Neurosci., 19.","DOI":"10.3389\/fncom.2025.1484540"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nagarajan, I., and Priya, G.G.L. (2025). A Comprehensive Review on Early Detection of Alzheimer\u2019s Disease Using Various Deep Learning Techniques. Front. Comput. Sci., 6.","DOI":"10.3389\/fcomp.2024.1404494"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s41060-023-00475-9","article-title":"Speech-Based Detection of Multi-Class Alzheimer\u2019s Disease Classification Using Machine Learning","volume":"18","author":"Tripathi","year":"2023","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1186\/s13195-025-01827-2","article-title":"Longitudinal Structural MRI-Based Deep Learning and Radiomics Features for Predicting Alzheimer\u2019s Disease Progression","volume":"17","author":"Aghajanian","year":"2025","journal-title":"Alzheimers Res. Ther."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Manap, A.S.A., Almadodi, R., Sultana, S., Sebastian, M.G., Kavani, K.S., Lyenouq, V.E., and Shankar, A. (2024). Alzheimer\u2019s Disease: A Review on the Current Trends of the Effective Diagnosis and Therapeutics. Front. Aging Neurosci., 16.","DOI":"10.3389\/fnagi.2024.1429211"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s12929-023-00976-6","article-title":"Clinical Trials of New Drugs for Alzheimer Disease: A 2020\u20132023 Update","volume":"30","author":"Huang","year":"2023","journal-title":"J. Biomed. Sci."},{"key":"ref_9","first-page":"1182","article-title":"Enhancing Alzheimer\u2019s Disease Diagnosis: The Efficacy of the YOLO Algorithm Model","volume":"14","author":"Byeon","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, Y., Al-Nusaif, M., Li, S., Tan, X., Yang, H., Cai, H., and Le, W. (2024). Progress on Early Diagnosing Alzheimer\u2019s Disease. Front. Med., 18.","DOI":"10.1007\/s11684-023-1047-1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ismail, Y.A., Auf, H.A., Sadik, S.A., and Ahmed, N.M. (2024). Evaluating Sensitivity, Specificity, and Optimal Cutoff Scores of the Montreal Cognitive Assessment Using U.S. National Alzheimer Coordinating Centre Uniform Data Set: A Retrospective Analysis of 16,309 Participants. Res. Sq., Preprint.","DOI":"10.21203\/rs.3.rs-4959701\/v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Young, V.M., Gates, S., Garcia, L.Y., and Salardini, A. (2025). Data Leakage in Deep Learning for Alzheimer\u2019s Disease Diagnosis: A Comprehensive Review of Methodological Rigor and Performance Inflation. Diagnostics, 15.","DOI":"10.3390\/diagnostics15182348"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vrahatis, A.G., Skolariki, K., Krokidis, M.G., Lazaros, K., Exarchos, T.P., and Vlamos, P. (2023). Revolutionizing the Early Detection of Alzheimer\u2019s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. Sensors, 23.","DOI":"10.3390\/s23094184"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Maleki, A., Zomorodi-Moghadam, M., and P\u0142awiak, P. (2024). Hybrid Distance-Aimed Triplet Loss for Automated Alzheimer\u2019s Disease Classification Using Feature Embeddings. Res. Sq., Preprint.","DOI":"10.21203\/rs.3.rs-4807617\/v1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"30273","DOI":"10.1038\/s41598-024-71358-7","article-title":"Revolutionizing Healthcare: A Comparative Insight into Deep Learning\u2019s Role in Medical Imaging","volume":"14","author":"Prasad","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15270","DOI":"10.1038\/s41598-024-60611-8","article-title":"Structure Focused Neurodegeneration Convolutional Neural Network for Modelling and Classification of Alzheimer\u2019s Disease","volume":"14","author":"Odimayo","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12559-023-10192-x","article-title":"Explainable Artificial Intelligence in Alzheimer\u2019s Disease Classification: A Systematic Review","volume":"16","author":"Viswan","year":"2024","journal-title":"Cogn. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/s42979-024-03012-y","article-title":"Correction: Alzheimer Disease Detection Using MRI: Deep Learning Review","volume":"5","author":"Saikia","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"25424823241311838","DOI":"10.1177\/25424823241311838","article-title":"Alzheimer\u2019s Disease Classification by Supervised and Intelligent Techniques","volume":"9","author":"Mourad","year":"2025","journal-title":"J. Alzheimer\u2019s Dis. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mehmood, M.H., Hassan, F., Rahman, A.U., Rauf, A., and Farooq, M.A. (2023, January 17\u201318). Alzr-net: A Novel Approach to Detect Alzheimer Disease. Proceedings of the 2023 International Conference on Communication, Computing and Digital Systems (C-CODE), Islamabad, Pakistan.","DOI":"10.1109\/C-CODE58145.2023.10139913"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Singh, R., Prabha, C., Dixit, H.M., and Kumari, S. (2023, January 18\u201320). Alzheimer Disease Detection Using Deep Learning. Proceedings of the 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India.","DOI":"10.1109\/ICSSAS57918.2023.10331661"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5551","DOI":"10.1109\/JBHI.2024.3412812","article-title":"Ensemble Vision Transformer for Dementia Diagnosis","volume":"28","author":"Huang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e60866","DOI":"10.2196\/60866","article-title":"Improved Alzheimer Disease Diagnosis with a Machine Learning Approach and Neuroimaging: Case Study Development","volume":"6","author":"Lazli","year":"2025","journal-title":"JMIRx Med."},{"key":"ref_24","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Parikh, A.P., T\u00e4ckstr\u00f6m, O., Das, D., and Uszkoreit, J. (2016). A Decomposable Attention Model for Natural Language Inference. arXiv.","DOI":"10.18653\/v1\/D16-1244"},{"key":"ref_26","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_27","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_28","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_29","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image Is Worth 16\u00d716 Words: Transformers for Image Recognition at Scale. arXiv, Available online: https:\/\/arxiv.org\/abs\/2010.11929."},{"key":"ref_30","first-page":"109","article-title":"TransBTS: Multimodal Brain Tumor Segmentation Using Transformer","volume":"Volume 12901","author":"Martel","year":"2021","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2021"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s12194-017-0406-5","article-title":"Overview of Deep Learning in Medical Imaging","volume":"10","author":"Suzuki","year":"2017","journal-title":"Radiol. Phys. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110057","DOI":"10.1016\/j.engappai.2025.110057","article-title":"Exploring the Synergies of Hybrid Convolutional Neural Network and Vision Transformer Architectures for Computer Vision: A Survey","volume":"144","author":"Haruna","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","unstructured":"Selvaraj, P. (2025, September 30). OASIS Dataset [Data Set]. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/pulavendranselvaraj\/oasis-dataset."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/754\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:49:02Z","timestamp":1772552942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,3]]},"references-count":33,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["diagnostics16050754"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16050754","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,3]]}}}