Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer’s disease. Lancet. 2021;397(10284):1577–90.

Article  Google Scholar 

Nguyen-Ky T, Wen P, Li Y. Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals. IEEE Trans Biomed Eng. 2013;60(6):1488–98.

Article  Google Scholar 

Schmierer T, Li T, Li Y. A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia. Health Inf Sci Syst. 2022;10(1):10.

Article  Google Scholar 

Siuly S, Li Y, Wen P, Alcin OF. SchizoGoogLeNet: the GoogLeNet-based deep feature extraction design for automatic detection of schizophrenia. Comput Intell Neurosci. 2022;2022(1):1992596.

Google Scholar 

Izzo J, Andreassen OA, Westlye LT, van der Meer D. The association between hippocampal subfield volumes in mild cognitive impairment and conversion to Alzheimer’s disease. Brain Res. 2020;1728: 146591.

Article  Google Scholar 

Li Y, Wen P, Powers D, Clark CR. LSB neural network based segmentation of MR brain images. In: IEEE SMC’99 conference proceedings. 1999 IEEE international conference on systems, man, and cybernetics (Cat. No. 99CH37028), 199, vol 6. IEEE; 1999. p. 822–5.

Bashar MR, Li Y, Wen P. Study of EEGs from somatosensory cortex and Alzheimer’s disease sources. Int J Biol Life Sci. 2011;8:2.

Google Scholar 

Li Y, Chi Z. MR brain image segmentation based on self-organizing map network. Int J Inf Technol. 2005;11(8).

Brickman AM, Zahodne LB, Guzman VA, Narkhede A, Meier IB, Griffith EY, Provenzano FA, Schupf N, Manly JJ, Stern Y, et al. Reconsidering harbingers of dementia: progression of parietal lobe white matter hyperintensities predicts Alzheimer’s disease incidence. Neurobiol Aging. 2015;36(1):27–32.

Article  Google Scholar 

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

Article  Google Scholar 

Sarki R, Ahmed K, Wang H, Zhang Y, Wang K. Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Trans Scalable Inf Syst. 2021. https://doi.org/10.4108/eai.16-12-2021.172436.

Article  Google Scholar 

Pandey D, Wang H, Yin X, Wang K, Zhang Y, Shen J. Automatic breast lesion segmentation in phase preserved DCE-MRIs. Health Inf Sci Syst. 2022;10(1):9.

Article  Google Scholar 

Wang S-H, Zhou Q, Yang M, Zhang Y-D. ADVIAN: Alzheimer’s disease VGG-inspired attention network based on convolutional block attention module and multiple way data augmentation. Front Aging Neurosci. 2021;13: 687456.

Article  Google Scholar 

Zhang Z, Gao L, Jin G, Guo L, Yao Y, Dong L, Han J, The Alzheimer’s Disease NeuroImaging Initiative. THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer’s disease. Quant Imaging Med Surg. 2021;11(7):3338–54.

Article  Google Scholar 

Guan H, Liu Y, Yang E, Yap P-T, Shen D, Liu M. Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Med Image Anal. 2021;71: 102076.

Article  Google Scholar 

Hoang GM, Kim U-H, Kim JG. Vision transformers for the prediction of mild cognitive impairment to Alzheimer’s disease progression using mid-sagittal sMRI. Front Aging Neurosci. 2023;15:1102869.

Article  Google Scholar 

Xing Y, Guan Y, Yang B, Liu J. Classification of sMRI images for Alzheimer’s disease by using neural networks. In: Yu S, Zhang Z, Yuen PC, Han J, Tan T, Guo Y, Lai J, Zhang J, editors. Pattern recognition and computer vision. Cham: Springer; 2022. p. 54–66.

Chapter  Google Scholar 

Zhang X, Han L, Zhu W, Sun L, Zhang D. An explainable 3D residual self-attention deep neural network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE J Biomed Health Inform. 2022;26(11):5289–97.

Article  Google Scholar 

Bakkouri I, Afdel K, Benois-Pineau J, Catheline G. Recognition of Alzheimer’s disease on sMRI based on 3D multi-scale CNN features and a gated recurrent fusion unit. In: 2019 International conference on content-based multimedia indexing (CBMI), 2019. IEEE; 2019. p. 1–6.

Chen L, Qiao H, Zhu F. Alzheimer’s disease diagnosis with brain structural MRI using multiview-slice attention and 3D convolution neural network. Front Aging Neurosci. 2022;14: 871706.

Article  Google Scholar 

Liu F, Wang H, Liang S-N, Jin Z, Wei S, Li X. MPS-FFA: a multiplane and multiscale feature fusion attention network for Alzheimer’s disease prediction with structural MRI. Comput Biol Med. 2023;157: 106790.

Article  Google Scholar 

Wei S, Li Y, Yang W. An adaptive feature fusion network for Alzheimer’s disease prediction. In: Health information science, lecture notes in computer science. Singapore: Springer; 2023. p. 271–82.

Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, Jack CR Jr, Jagust WJ, Shaw LM, Toga AW, Trojanowski JQ, Weiner MW. Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neurology. 2010;74(3):201–9.

Article  Google Scholar 

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations, 2015.

Han R, Liu Z, Chen CLP. Multi-scale 3D convolution feature-based broad learning system for Alzheimer’s disease diagnosis via MRI images. Appl Soft Comput. 2022;120: 108660.

Article  Google Scholar 

Hu K, Wang Y, Chen K, Hou L, Zhang X. Multi-scale features extraction from baseline structure MRI for MCI patient classification and ad early diagnosis. Neurocomputing. 2016;175:132–45.

Article  Google Scholar 

Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJ, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems. New York: Curran Associates Inc; 2012. p. 25.

Google Scholar 

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Advances in neural information processing systems, 2017, p. 30.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), 2016. IEEE; 2016. p. 770–778.

Banerjee K, Prasad CV, Gupta RR, Vyas K, Anushree H, Mishra B. Exploring alternatives to Softmax function. In: Proceedings of the 2nd international conference on deep learning theory and applications (DeLTA 2021), 2020.

Aljalbout E, Golkov V, Siddiqui Y, Strobel M, Cremers D. Clustering with deep learning: taxonomy and new methods. arXiv preprint; 2018. arXiv:1801.07648.

Liang D, Lin L, Hu H, Zhang Q, Chen Q, lwamoto Y, Han X, Chen Y-W. Combining convolutional and recurrent neural networks for classification of focal liver lesions in multi-phase CT images. In: Medical image computing and computer assisted intervention—MICCAI 2018, 2018. Cham: Springer; 2018. p. 666–75.

Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021;65(5):545–63.

Article  Google Scholar 

Prechelt L. Early stopping—but when? In: Neural Networks: tricks of the trade, 2022. Springer; 2002. p. 55–69.

Bernerth JB, Aguinis H. A critical review and best-practice recommendations for control variable usage. Pers Psychol. 2016;69(1):229–83.

Article  Google Scholar 

Nanni L, Brahnam S, Salvatore C, Castiglioni I. Texture descriptors and voxels for the early diagnosis of Alzheimer’s disease. Artif Intell Med. 2019;97:19–26.

Article  Google Scholar 

Gao X, Shi F, Shen D, Liu M. Task-induced pyramid and attention GAN for multimodal brain image imputation and classification in Alzheimer’s disease. IEEE J Biomed Health Inform. 2021;26(1):36–43.

Article  Google Scholar 

Lian C, Liu M, Pan Y, Shen D. Attention-guided hybrid network for dementia diagnosis with structural MR images. IEEE Trans Cybern. 2020;52(4):1992–2003.

Article  Google Scholar 

Guan H, Wang C, Cheng J, Jing J, Liu T. A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer’s disease. Hum Brain Mapp. 2022;43(2):760–72.

Article  Google Scholar 

Zhu W, Sun L, Huang J, Han L, Zhang D. Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI. IEEE Trans Med Imaging. 2021;40(9):2354–66.

Article  Google Scholar 

Salami F, Bozorgi-Amiri A, Hassan GM, Tavakkoli-Moghaddam R, Datta A. Designing a clinical decision support system for Alzheimer’s diagnosis on OASIS-3 data set. Biomed Signal Process Control. 2022;74: 103527.

Article  Google Scholar 

Islam J, Zhang Y. Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inform. 2018;5:1–14.

Article  Google Scholar 

Saratxaga CL, Moya I, Picón A, Acosta M, Moreno-Fernandez-de-Leceta A, Garrote E, Bereciartua-Perez A. MRI deep learning-based solution for Alzheimer’s disease prediction. J Pers Med. 2021;11(9):902.

Article  Google Scholar 

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, p. 770–8.

Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861–74.

Article  MathSciNet  Google Scholar 

Zheng B, Gao A, Huang X, Li Y, Liang D, Long X. A modified 3D EfficientNet for the classification of Alzheimer’s disease using structural magnetic resonance images. IET Image Process. 2023;17(1):77–87.

Article  Google Scholar 

Mehmood A, Maqsood M, Bashir M, Shuyuan Y. A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci. 2020;10(2):84.

Article  Google Scholar 

Tufail AB, Ma Y-K, Zhang Q-N. Binary classification of Alzheimer’s disease using sMRI imaging modality and deep learning. J Digit Imaging. 2020;33:1073–90.

Article  Google Scholar 

Möller C, Vrenken H, Jiskoot L, Versteeg A, Barkhof F, Scheltens P, van der Flier WM. Different patterns of gray matter atrophy in early- and late-onset Alzheimer’s disease. Neurobiol Aging. 2013;34(8):2014–22.

Article  Google Scholar 

Yang J, Pan P, Song W, Huang R, Li J, Chen K, Gong Q, Zhong J, Shi H, Shang H. Voxelwise meta-analysis of gray matter anomalies in Alzheimer’s disease and mild cognitive impairment using anatomic likelihood estimation. J Neurol Sci. 2012;316(1–2):21–9.

Article  Google Scholar 

Xia M, Wang J, He Y. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS ONE. 2013;8(7): e68910.

Article  Google Scholar 

Sadiq MT, Siuly S, Almogren A, Li Y, Wen P. Efficient novel network and index for alcoholism detection from EEGs. Health Inf Sci Syst. 2023;11(1):27.

Article  Google Scholar 

Li Y, Wen P, et al. Analysis and classification of EEG signals using a hybrid clustering technique. In: IEEE/ICME international conference on complex medical engineering, 2010. IEEE; 2010. p. 34–9.