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REVIEW 4 major objections 4 minor 45 references

Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that treating 3D brain MRI scans as videos and applying a video vision transformer classifies Alzheimer's disease, mild cognitive impairment, and normal cognition with 98.6% accuracy on the ADNI1 Complete 3Yr 3T dataset.

desk verdict A reasonable video-transformer-on-3D-MRI demo whose headline accuracy claim is undercut by a contradictory evaluation protocol, unmatched baseline training, and an unsupported 'significant' conclusion. read the letter →

arxiv 2501.15733 v1 pith:VLCILV6D submitted 2025-01-27 eess.IV cs.AIcs.CV

Taymaz Akan , Sait Alp , Md. Shenuarin Bhuiyan , Elizabeth A. Disbrow , Steven A. Conrad , John A. Vanchiere , Christopher G. Kevil , Mohammad A. N. Bhuiyan This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords Alzheimer'sdiseasemildcognitiveimpairmentvideovisiontransformerViT3DbrainMRImulticlassclassificationspatio-temporalattentionADNI
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces ViTranZheimer, a deep learning model that reinterprets a 3D brain MRI scan as a video—each slice a frame—and applies a video vision transformer to classify the scan as normal cognition, mild cognitive impairment, or Alzheimer's disease. The authors report 98.6% accuracy on the ADNI1 Complete 3Yr 3T dataset, outperforming two hybrid baselines, CNN-BiLSTM (96.479%) and ViT-BiLSTM (97.465%). The central idea is that treating the whole 3D volume as a spatio-temporal sequence lets the model capture inter-slice relationships end-to-end, rather than extracting slice features separately and then modeling sequences. If true, this gives clinicians a non-invasive, automated tool for early AD detection, which matters because current treatments target early-stage disease.

What carries the argument

The central mechanism is tubelet-based spatio-temporal attention. The 3D MRI volume of shape $32 \times 64 \times 64$ is partitioned into non-overlapping 3D tubelets of size $32 \times 16 \times 16$; each tubelet is flattened, linearly embedded, and summed with positional encodings. The resulting token sequence enters a transformer encoder with 16 heads and 16 layers, where multi-head self-attention computes relationships across all tubelets, capturing both within-slice spatial patterns and between-slice dependencies. A CLS token aggregates global information for classification. This replaces the two-stage pipeline of independent slice feature extraction followed by a recurrent sequence model with a single end-to-end spatio-temporal model.

What would settle it

Train the same three architectures on the same ADNI1 Complete 3Yr 3T data using subject-level cross-validation, ensuring no scan from the same subject appears in both training and test sets, and report test accuracy. If ViTranZheimer's accuracy falls to within the baseline's standard deviation, or if the original split contains scans from the same subject in both train and test, the claimed advantage collapses. A simpler check: compute a paired test or 95% confidence interval over the 10 repeat runs; if the intervals overlap, the 'statistically significant' claim is not supported by the reported numbers.

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Extended reading notes

Core claim

ViTranZheimer uses the ViViT video vision transformer to classify 3D T1-weighted brain MRI volumes. Instead of splitting the brain into independent 2D slices and feeding them to a CNN or ViT plus a BiLSTM, it divides the entire volume into 3D tubelets that span both space and adjacent slices, embeds them, and applies multi-head spatio-temporal self-attention. The final CLS token is passed to a softmax head for three-way classification of normal cognition, mild cognitive impairment, and Alzheimer's disease. On the ADNI1 Complete 3Yr 3T dataset of 351 scans, the model is reported to achieve 98.6% accuracy with 0.97 precision, recall, and F-score, outperforming the CNN-BiLSTM and ViT-BiLSTM hybrids. The paper concludes that this accuracy advantage is statistically significant and that video vision transformers are a promising end-to-end framework for AD diagnosis.

Load-bearing premise

The reported accuracy gap rests on the evaluation protocol being unbiased: the random 60/20/20 split and repeated 10-fold stratified cross-validation must have no data leakage and the baseline models must be trained with comparable effort, or the 1.1-2.1 point accuracy difference loses meaning.

Editorial extensions

If this is right

  • If the result holds, a video vision transformer can classify three-way Alzheimer's status (normal, MCI, AD) from a single structural MRI at 98.6% accuracy, exceeding the two hybrid baselines tested in the same study.
  • The end-to-end design removes the need for separate slice-level feature extraction and sequence modeling, simplifying the training pipeline for 3D medical images.
  • The high sensitivity for MCI, the intermediate disease stage, supports the prospect of automated early screening from routine MRI, which could prompt earlier clinical follow-up.
  • Because the dataset is a publicly available standard collection, other researchers can directly compare their methods against ViTranZheimer on the same data.
  • The reported accuracy on this dataset exceeds the published results listed in the paper's comparison table for other multiclass AD classification methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next test, not run in the paper, is subject-level cross-validation: splitting by patient rather than by scan, since multiple scans from one subject could inflate accuracy if they leak across train and test sets.
  • The same tubelet-based spatio-temporal design could transfer to other 3D medical volumes such as CT or fMRI, where inter-slice dependencies carry diagnostic information; the paper only demonstrates MRI.
  • The paper mentions MRI harmonization as future work; combining the model with harmonized multi-scanner data would test whether the 98.6% accuracy transfers across acquisition sites and scanning protocols.
  • A paired statistical test over the repeated cross-validation runs would clarify whether the accuracy gap over baselines is reliable, since the reported standard deviations of the three models overlap.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The manuscript proposes ViTranZheimer, a video vision transformer for three-class classification (NC/MCI/AD) of 3D brain MRI from the ADNI1 Complete 3Yr 3T dataset, treating MRI slices as video frames. It reports 98.6% accuracy for ViTranZheimer versus 96.479% for CNN-BiLSTM and 97.465% for ViT-BiLSTM, and concludes that this is a statistically significant advantage. The paper describes preprocessing, the ViViT architecture with spatio-temporal attention, training hyperparameters, and comparisons with baselines and prior work.

Significance. If the result were reliably established, applying video transformers to whole MRI volumes would be a useful contribution to Alzheimer's disease diagnosis. The paper has strengths: it uses a publicly available standard ADNI dataset, proposes an end-to-end trainable model, and provides architectural details and equations. However, the evaluation is undermined by contradictory protocol descriptions, uncontrolled baseline training settings, and the absence of any statistical test for the claimed significance. As it stands, the headline accuracy is not a trustworthy estimate and the main claim of superiority is not supported.

major comments (4)
  1. [Section 2.1 vs Section 3.1] The evaluation protocol is described inconsistently. Section 2.1 states that the 351 scans were 'randomly split into 60% training, 20% testing, and 20% validation sets,' while Section 3.1 states that 'Repeated 10-fold stratified cross-validation and testing were adopted' and then says 'each sample of all the datasets was only used for the test once,' which is a single 10-fold cross-validation rather than a repeated one. These protocols imply different test set sizes (about 70 scans for the 60/20/20 split versus 351 scans for 10-fold CV) and therefore different error counts for the reported 98.6% accuracy. The manuscript must specify one unambiguous protocol and report results consistently with it.
  2. [Section 2.4 and Section 3.1] The model selection procedure is not clearly tied to the evaluation protocol. The text says the model checkpoint is saved when validation loss improves, but it is not stated whether that validation set is the fixed 20% split described in Section 2.1 or a fold within cross-validation. If any portion of the test set was used to select the checkpoint, the reported accuracy is optimistically biased. The authors should define exactly how the training, validation, and test partitions are created and how checkpoint selection interacts with the reported accuracy.
  3. [Section 3.1 and Table 4] The baseline comparisons are not controlled. CNN-BiLSTM and ViT-BiLSTM use ImageNet-pretrained feature extractors, are trained for 100 epochs with batch size 25, and are described as processing slices, whereas ViTranZheimer is trained from scratch for 1500 epochs with batch size 128 and operates on the whole volume. Table 4 gives hyperparameters only for the CNN-BiLSTM baseline and does not specify the ViT-BiLSTM configuration. The reported accuracy differences (1-2 percentage points) could be due to training budget, pretraining, or input representation rather than architectural superiority. A matched-budget comparison or a sensitivity analysis is needed to support the claim that ViTranZheimer outperforms the baselines.
  4. [Section 5 and Section 3.2] The conclusion asserts a 'statistically significant advantage' for ViTranZheimer, but no statistical test is reported anywhere in Section 3.2. With 351 samples and accuracies of 98.6% versus 97.465%, the difference may not be significant; the authors should provide a paired test (e.g., McNemar's test on the same folds) or confidence intervals for the accuracy difference. Without such a test, the significance claim is unsupported.
minor comments (4)
  1. [Table 6] Table 6 labels the proposed method as 'Slice-based' even though Section 2.3 describes processing the entire 3D volume through tubelets; this labeling is inconsistent and should be corrected.
  2. [Section 4] The discussion contains the typo 'using 3CNN' where '3D CNN' is intended; please correct this.
  3. [Figure 4] The confusion matrices in Figure 4 are too small to read in the manuscript; they should be enlarged or replotted for legibility.
  4. [Replication of results] The replication statement says code and data are 'available on request'; for reproducibility, the authors should provide code, trained model weights, and the exact data split or cross-validation indices.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported accuracy is an empirical measurement from a trained model and no load-bearing claim reduces to its inputs by construction.

full rationale

This is an empirical deep-learning study, not a derivation, so the central circularity patterns do not apply. The 98.6% accuracy, precision, recall, and F-score are measured outcomes of a trained ViViT-based classifier on ADNI data, not quantities defined in terms of the fitted parameters in a way that forces the result. The architecture description (tubelet embedding, attention, FFN, softmax head) is standard and does not presuppose the reported accuracy. The paper does cite the authors' own prior work for the ViT-BiLSTM baseline and the CNN-BiLSTM baseline, and it motivates the end-to-end approach by contrast with the earlier two-stage method; however, these citations are used as comparison systems or background, not as a validation that makes the present claim true by appeal to the same authors. No uniqueness theorem or ansatz is imported from self-citations to force the model choice. There are legitimate evaluation-protocol concerns that are not circularity: Section 2.1 describes a single random 60/20/20 split while Section 3.1 describes repeated 10-fold stratified cross-validation, the number of test samples consistent with 98.6% accuracy is ambiguous, baseline training budgets differ (100 epochs with pretrained extractors vs. 1500 epochs end-to-end), and the Conclusion asserts a statistically significant advantage without reporting a significance test. These issues undermine verifiability and comparability, but they do not show that any prediction is equivalent to its input by definition. Therefore the circularity score is 0.

Assumptions & free parameters 8 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities; all model components come from existing ViViT and baseline architectures. Its contributions rest on hand-chosen hyperparameters and domain assumptions about the dataset and preprocessing, but no new physical or mathematical objects.

free parameters (8)
  • Number of central slices selected = 32
    Only the central 32 slices are kept after MNI normalization; the choice is manual and affects the input and result.
  • Tubelet/patch size = (32, 16, 16)
    Tokenization dimensions for the video transformer, chosen by hand.
  • Number of transformer layers = 16
    Model depth hyperparameter, set manually.
  • Number of attention heads = 16
    Model width hyperparameter, set manually.
  • Projection dimension = 32
    Embedding size for token projections, set manually.
  • Learning rate = 1e-4
    Adam optimizer learning rate, chosen by hand.
  • Batch size = 128
    Training batch size, chosen by hand.
  • Training epochs for ViViT = 1500
    Number of epochs for the proposed model, far exceeding the 100 epochs for baselines, affecting comparison fairness.
assumptions (4)
  • domain assumption The ADNI1 Complete 3Yr 3T dataset is representative of the broader population for Alzheimer's disease classification.
    The paper relies on this single publicly available dataset without external validation.
  • domain assumption The preprocessing pipeline (CAT12 segmentation, skull stripping, MNI normalization, central slice selection) preserves diagnostically relevant information.
    No ablation is shown to justify the 32-slice selection or the preprocessing choices.
  • domain assumption Treating the spatial slice axis as a temporal dimension is a valid inductive bias for the ViViT model.
    The paper assumes inter-slice dependencies in MRI function like temporal dynamics in video, which is a modeling choice without empirical justification.
  • standard math Standard deep learning training assumptions (i.i.d. samples, no distribution shift between train and test) hold.
    Needed for the reported accuracy to generalize to unseen data.

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Cite this review

Pith. "Pith review of Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI." pith.science (2026). https://pith.science/paper/VLCILV6D

@misc{pith2026250115733,
  author       = {Pith},
  title        = {Pith review of: Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VLCILV6D}},
  note         = {Machine review of arXiv:2501.15733}
}
read the original abstract

Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions worldwide, necessitating early and accurate diagnosis for optimal patient management. In recent years, advancements in deep learning have shown remarkable potential in medical image analysis. Methods In this study, we present "ViTranZheimer," an AD diagnosis approach which leverages video vision transformers to analyze 3D brain MRI data. By treating the 3D MRI volumes as videos, we exploit the temporal dependencies between slices to capture intricate structural relationships. The video vision transformer's self-attention mechanisms enable the model to learn long-range dependencies and identify subtle patterns that may indicate AD progression. Our proposed deep learning framework seeks to enhance the accuracy and sensitivity of AD diagnosis, empowering clinicians with a tool for early detection and intervention. We validate the performance of the video vision transformer using the ADNI dataset and conduct comparative analyses with other relevant models. Results The proposed ViTranZheimer model is compared with two hybrid models, CNN-BiLSTM and ViT-BiLSTM. CNN-BiLSTM is the combination of a convolutional neural network (CNN) and a bidirectional long-short-term memory network (BiLSTM), while ViT-BiLSTM is the combination of a vision transformer (ViT) with BiLSTM. The accuracy levels achieved in the ViTranZheimer, CNN-BiLSTM, and ViT-BiLSTM models are 98.6%, 96.479%, and 97.465%, respectively. ViTranZheimer demonstrated the highest accuracy at 98.6%, outperforming other models in this evaluation metric, indicating its superior performance in this specific evaluation metric. Conclusion This research advances the understanding of applying deep learning techniques in neuroimaging and Alzheimer's disease research, paving the way for earlier and less invasive clinical diagnosis.

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Works this paper leans on

45 extracted references · 34 canonical work pages

  1. [1]

    Rahim, S

    N. Rahim, S. El -Sappagh, S. Ali, K. Muhammad, J. Del Ser, T. Abuhmed, Prediction of Alzheimer’s progression based on multimodal Deep -Learning-based fusion and visual Explainability of time - series data, Information Fusion 92 (2023) 363 –388. https://doi.org/10.1016/J.INFFUS.2022.11.028

    work page doi:10.1016/j.inffus.2022.11.028 2023

  2. [2]

    Zhang, S

    Y. Zhang, S. Wang, K. Xia, Y. Jiang, P. Qian, Alzheimer’s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion, Information Fusion 66 (2021) 170 –183. https://doi.org/10.1016/J.INFFUS.2020.09.002

    work page doi:10.1016/j.inffus.2020.09.002 2021

  3. [3]

    Castillo-Barnes, L

    D. Castillo-Barnes, L. Su, J. Ramírez, D. Salas -Gonzalez, F.J. Martinez-Murcia, I.A. Illan, F. Segovia, A. Ortiz, C. Cruchaga, M.R. Farlow, C. Xiong, N.R. Graff -Radford, P.R. Schofield, C.L. Masters, S. Salloway, M. Jucker, H. Mori, J. Levin, J.M. Gorriz, D.I.A.N. (DIAN), Autosomal dominantly inherited alzheimer disease: Analysis of genetic subgroups by...

    work page doi:10.1016/j.inffus.2020.01.001 2020

  4. [4]

    J. Albright, Forecasting the progression of Alzheimer’s disease using neural networks and a novel preprocessing algorithm, Alzheimer’s & Dementia: Translational Research & Clinical Interventions 5 (2019) 483–491. https://doi.org/10.1016/J.TRCI.2019.07.001

    work page doi:10.1016/j.trci.2019.07.001 2019

  5. [5]

    Loddo, S

    A. Loddo, S. Buttau, C. Di Ruberto, Deep learning based pipelines for Alzheimer’s disease diagnosis: A comparative study and a novel deep -ensemble method, Comput Biol Med 141 (2022) 105032. https://doi.org/10.1016/J.COMPBIOMED.2021.105032

    arXiv 2022

  6. [6]

    Martinez-Murcia, J.M

    F.J. Martinez-Murcia, J.M. Górriz, J. Ramírez, A. Ortiz, A Structural Parametrization of the Brain Using Hidden Markov Models -Based Paths in Alzheimer’s Disease, Https://Doi.Org/10.1142/S0129065716500246 26 (2016). https://doi.org/10.1142/S0129065716500246

    work page doi:10.1142/s0129065716500246 2016

  7. [7]

    Abrol, M

    A. Abrol, M. Bhattarai, A. Fedorov, Y. Du, S. Plis, V. Calhoun, Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease, J Neurosci Methods 339 (2020) 108701. https://doi.org/10.1016/J.JNEUMETH.2020.108701

    work page arXiv 2020

  8. [8]

    Mall, P.K

    P.K. Mall, P.K. Singh, S. Srivastav, V. Narayan, M. Paprzycki, T. Jaworska, M. Ganzha, A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities, Healthcare Analytics 4 (2023) 100216. https://doi.org/10.1016/J.HEALTH.2023.100216

    arXiv 2023

Show all 45 references
  1. [9]

    Jonsson, G

    B.A. Jonsson, G. Bjornsdottir, T.E. Thorgeirsson, L.M. Ellingsen, G.B. Walters, D.F. Gudbjartsson, H. Stefansson, K. Stefansson, M.O. Ulfarsson, Brain age prediction using deep learning uncovers associated sequence variants, Nature Communications 2019 10:1 10 (2019) 1 –10. htt...

    2019 doi

  2. [10]

    Boutet, R

    A. Boutet, R. Madhavan, G.J.B. Elias, S.E. Joel, R. Gramer, M. Ranjan, V. Paramanandam, D. Xu, J. Germann, A. Loh, S.K. Kalia, M. Hodaie, B. Li, S. Prasad, A. Coblentz, R.P. Munhoz, J. Ashe, W. Kucharczyk, A. Fasano, A.M. Lozano, Predicting optimal de ep brain stimulation para...

    2021 doi

  3. [11]

    B. Zhao, H. Lu, S. Chen, J. Liu, D. Wu, Convolutional neural networks for time series classification, Journal of Systems Engineering and Electronics 28 (2017) 162 –169. https://doi.org/10.21629/JSEE.2017.01.18

    2017 doi

  4. [12]

    L. Yue, X. Gong, J. Li, H. Ji, M. Li, A.K. Nandi, Hierarchical feature extraction for early Alzheimer’s disease diagnosis, IEEE Access 7 (2019) 93752 –93760. https://doi.org/10.1109/ACCESS.2019.2926288

    2019

  5. [13]

    Silva, G.S.L

    I.R.R. Silva, G.S.L. Silva, R.G. De Souza, W.P. Dos Santos, R.A.A. De Fagundes, Model Based on Deep Feature Extraction for Diagnosis of Alzheimer’s Disease, Proceedings of the International Joint Conference on Neural Networks 2019-July (2019). https://doi.org/10.1109/IJCNN.201...

    2019

  6. [14]

    Zhang, Z

    F. Zhang, Z. Li, B. Zhang, H. Du, B. Wang, X. Zhang, Multi -modal deep learning model for auxiliary diagnosis of Alzheimer’s disease, Neurocomputing 361 (2019) 185 –195. https://doi.org/10.1016/J.NEUCOM.2019.04.093

    2019 doi

  7. [15]

    Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun, Transformers in Time Series: A Survey, (2022). https://doi.org/10.48550/arxiv.2202.07125

    2022 doi arXiv

  8. [16]

    Krizhevsky, I

    A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, Commun ACM 60 (2017) 84–90. https://doi.org/10.1145/3065386

    2017 doi

  9. [17]

    Redmon, S

    J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You Only Look Once: Unified, Real -Time Object Detection, (2016) 779–788

    2016

  10. [18]

    Wojke, A

    N. Wojke, A. Bewley, D. Paulus, Simple online and realtime tracking with a deep association metric, Proceedings - International Conference on Image Processing, ICIP 2017 -September (2018) 3645–

    2018

  11. [19]

    Ronneberger, P

    O. Ronneberger, P. Fischer, T. Brox, U -net: Convolutional networks for biomedical image segmentation, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9351 (2015) 234 –241. https://doi.org/10....

    2015 doi

  12. [20]

    J. Jang, D. Hwang, M3T: Three-Dimensional Medical Image Classifier Using Multi-Plane and Multi- Slice Transformer, (2022) 20718–20729

    2022

  13. [21]

    Fathi, · Ali Ahmadi, · Afsaneh Dehnad, · Mostafa, A

    S. Fathi, · Ali Ahmadi, · Afsaneh Dehnad, · Mostafa, A. - Dooghaee, M. Sadegh, A Deep Learning - Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images, Neuroinformatics 2023 1 (2023) 1–17. https://doi.org/10.1007/S12021-023-09646-2

    2023 doi

  14. [22]

    Y. Lyu, X. Yu, D. Zhu, L. Zhang, Classification of alzheimer’s disease via vision transformer: Classification of alzheimer’s disease via vision transformer, in: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, 2022: ...

    2022

  15. [23]

    Odusami, R

    M. Odusami, R. Maskeliūnas, R. Damaševičius, Pixel-level fusion approach with vision transformer for early detection of Alzheimer’s disease, Electronics (Basel) 12 (2023) 1218

    2023

  16. [24]

    Hoang, U

    G.M. Hoang, U. -H. Kim, J.G. Kim, Vision transformers for the prediction of mild cognitive impairment to Alzheimer’s disease progression using mid -sagittal sMRI, Front Aging Neurosci 15 (2023) 1102869

    2023

  17. [25]

    Castro-Silva, M.N

    J.A. Castro-Silva, M.N. Moreno-García, D.H. Peluffo-Ordóñez, Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer, Mathematics 12 (2024) 2720

    2024

  18. [26]

    Saoud, H

    L.S. Saoud, H. AlMarzouqi, Explainable Early Detection of Alzheimer’s Disease Using ROIs and an Ensemble of 3D Vision Transformers, (2024)

    2024

  19. [27]

    S. Alp, T. Akan, M.S. Bhuiyan, E.A. Disbrow, S.A. Conrad, J.A. Vanchiere, C.G. Kevil, M.A.N. Bhuiyan, Joint transformer architecture in brain 3D MRI classification: its application in Alzheimer’s disease classification, Sci Rep 14 (2024) 8996

    2024

  20. [28]

    Litjens, T

    G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A. Van Der Laak, B. Van Ginneken, C.I. Sánchez, A survey on deep learning in medical image analysis, Med Image Anal 42 (2017) 60–88

    2017

  21. [29]

    https://adni.loni.usc.edu/ (accessed April 3, 2023)

    ADNI | Alzheimer’s Disease Neuroimaging Initiative, (n.d.). https://adni.loni.usc.edu/ (accessed April 3, 2023)

    2023

  22. [30]

    Jack, M.A

    C.R. Jack, M.A. Bernstein, N.C. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P.J. Britson, J.L. Whitwell, C. Ward, A.M. Dale, J.P. Felmlee, J.L. Gunter, D.L.G. Hill, R. Killiany, N. Schuff, S. Fox- Bosetti, C. Lin, C. Studholme, C.S. DeCarli , G. Krueger, H.A. Ward,...

    2008

  23. [31]

    Ashburner, K.J

    J. Ashburner, K.J. Friston, Unified segmentation, Neuroimage 26 (2005) 839 –851. https://doi.org/10.1016/J.NEUROIMAGE.2005.02.018

    2005 doi

  24. [32]

    Arnab, M

    A. Arnab, M. Dehghani, G. Heigold, C. Sun, M.L. Luči´c, C. Schmid, ViViT: A Video Vision Transformer, (n.d.)

  25. [33]

    T. Akan, S. Alp, M.A.N. Bhuiyan, Vision Transformers and Bi-LSTM for Alzheimer’s Disease Diagnosis from 3D MRI, in: 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), IEEE, 2023: pp. 530–535

    2023

  26. [34]

    Billones, O.J.L.D

    C.D. Billones, O.J.L.D. Demetria, D.E.D. Hostallero, P.C. Naval, DemNet: A Convolutional Neural Network for the detection of Alzheimer’s Disease and Mild Cognitive Impairment, IEEE Region 10 Annual International Conference, Proceedings/TENCON (2017) 3724 –3727. https://doi.org...

    2017

  27. [35]

    Hosseini-Asl, … R.K

    E. Hosseini-Asl, … R.K. -2016 I. international, undefined 2016, Alzheimer’s disease diagnostics by adaptation of 3D convolutional network, Ieeexplore.Ieee.Org (n.d.). https://ieeexplore.ieee.org/abstract/document/7532332/?casa_token=Neb5n7ikTZMAAAAA:PkE GLJT7qw9U49OS9KRibb0AFV...

    2016

  28. [36]

    Valliani, A.S.-P

    A. Valliani, A.S.-P. of the 8th A. international conference, undefined 2017, Deep residual nets for improved Alzheimer’s diagnosis, Dl.Acm.Org (2017) 615. https://doi.org/10.1145/3107411.3108224

    2017

  29. [37]

    Gunawardena, … R.R.-… on M

    K. Gunawardena, … R.R.-… on M. and, undefined 2017, Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural MRI data, Ieeexplore.Ieee.Org (n.d.). https://ieeexplore.ieee.org/abstract/document/8211486/?casa_token=0Vm5OBjwvlYAAAAA:PKV bNMA...

    2017

  30. [38]

    Karasawa, C.L

    H. Karasawa, C.L. Liu, H. Ohwada, Deep 3D Convolutional Neural Network Architectures for Alzheimer’s Disease Diagnosis, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics ) 10751 LNAI (2018) 287 –...

    2018 doi

  31. [39]

    T.D. Vu, N.H. Ho, H.J. Yang, J. Kim, H.C. Song, Non -white matter tissue extraction and deep convolutional neural network for Alzheimer’s disease detection, Soft Comput 22 (2018) 6825 –

    2018

  32. [40]

    R. Jain, N. Jain, A. Aggarwal, D.H. -C.S. Research, undefined 2019, Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images, Elsevier (n.d.). https://www.sciencedirect.com/science/article/pii/S1389041718309562 (accessed March ...

    2019

  33. [41]

    H. Wang, Y. Shen, S. Wang, T. Xiao, L. Deng, X. Wang, X. Zhao, Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease, Neurocomputing 333 (2019) 145–156. https://doi.org/10.1016/J.NEUCOM.2018.12.018

    2019 doi

  34. [42]

    Goenka, S

    N. Goenka, S. Tiwari, AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer’s disease through multiple neuroimaging computational approaches, Biomed Signal Process Control 74 (2022) 103500. https://doi.org/10.1016/J.BSPC.2022.103500

    2022

  35. [43]

    Odusami, R

    M. Odusami, R. Maskeliūnas, R. Damaševičius, An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging, Sensors 2022, Vol. 22, Page 740 22 (2022) 740. https://doi.org/10.3390/S22030740. Taymaz Akan received his Ph.D. from the Department of Com...

    2022 doi

  36. [3649]

    https://doi.org/10.1109/ICIP.2017.8296962

    2017

  37. [6833]

    https://doi.org/10.1007/S00500-018-3421-5

    doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.