Leveraging deep learning for early diagnosis of Alzheimer’s disease via brain MRI imaging

Scheltens P, et al. Alzheimer’s disease. Lancet. 2021;397(10284):1577–90. https://doi.org/10.1016/S0140-6736(20)32205-4.

Article  Google Scholar 

Alzheimer’s Disease Fact Sheet. National institute on aging. 2023. https://www.nia.nih.gov/health/alzheimers-and-dementia/alzheimers-disease-fact-sheet

Maresova P, Mohelska H, Dolejs J, Kuca K. Socio-economic aspects of Alzheimer’s disease. Curr Alzheimer Res. 2015;12(9):903–11. https://doi.org/10.2174/156720501209151019111448.

Article  Google Scholar 

Aisen PS, et al. Report of the task force on designing clinical trials in early (predementia) AD. Neurology. 2011;76(3):280–6. https://doi.org/10.1212/WNL.0B013E318207B1B9.

Article  Google Scholar 

Zhao G, Zhang H, Xu Y, Chu X. Research on magnetic resonance imaging in diagnosis of Alzheimer’s disease. Eur J Med Res. 2024;29(1):632. https://doi.org/10.1186/S40001-024-02172-0/FIGURES/2.

Article  Google Scholar 

Maurer K, Volk S, Gerbaldo H. Auguste D and Alzheimer’s disease. Lancet. 1997;349(9064):1546–9. https://doi.org/10.1016/S0140-6736(96)10203-8.

Article  Google Scholar 

Shamrat FMJM, et al. Alzheimernet: an effective deep learning based proposition for Alzheimer’s disease stages classification from functional brain changes in magnetic resonance images. IEEE Access. 2023;11:16376–95. https://doi.org/10.1109/ACCESS.2023.3244952.

Article  Google Scholar 

Batta I, Abrol A, Calhoun V. A multimodal deep learning approach for automated detection and characterization of distinctly salient features of Alzheimers disease. In: 2023 IEEE 20th International symposium on biomedical imaging (ISBI). IEEE; 2023. pp. 1–4. https://doi.org/10.1109/ISBI53787.2023.10230525.

Divya R, ShanthaSelvaKumari R. SUVR quantification using attention-based 3D CNN with longitudinal Florbetapir PET images in Alzheimer’s disease. Biomed Signal Process Control. 2023;86:105254. https://doi.org/10.1016/j.bspc.2023.105254.

Article  Google Scholar 

Skin cancer detection based on deep learning. J Biomed Phys Eng. 2022;12(6). https://doi.org/10.31661/jbpe.v0i0.2207-1517.

Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep convolutional neural networks in medical image analysis: a review. Information. 2025;16(3):195. https://doi.org/10.3390/INFO16030195.

Article  Google Scholar 

Khan Mamun MMR, Elfouly T. Detection of cardiovascular disease from clinical parameters using a one-dimensional convolutional neural network. Bioengineering. 2023;10(7):796. https://doi.org/10.3390/bioengineering10070796.

Article  Google Scholar 

Gaudiuso R, Ewusi-Annan E, Xia W, Melikechi N. Diagnosis of Alzheimer’s disease using laser-induced breakdown spectroscopy and machine learning. Spectrochim Acta B At Spectrosc. 2020;171:105931. https://doi.org/10.1016/j.sab.2020.105931.

Article  Google Scholar 

Pais MV, Forlenza OV, Diniz BS. Plasma biomarkers of Alzheimer’s disease: a review of available assays, recent developments, and implications for clinical practice. J Alzheimers Dis Rep. 2023;7(1):355–80. https://doi.org/10.3233/ADR-230029.

Article  Google Scholar 

Sharma G, Vijayvargiya A, Kumar R. Comparative assessment among different convolutional neural network architectures for Alzheimer’s disease detection. In: 2021 IEEE 8th Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON). IEEE; 2021. pp. 1–6. https://doi.org/10.1109/UPCON52273.2021.9667607.

Hasan ME, Wagler A. A novel deep learning graph attention network for Alzheimer’s disease image segmentation. Healthcare Analytics. 2024;5:100310. https://doi.org/10.1016/j.health.2024.100310.

Article  Google Scholar 

Leela M, Helenprabha K, Sharmila L. Prediction and classification of Alzheimer disease categories using integrated deep transfer learning approach. Meas Sensors. 2023;27:100749. https://doi.org/10.1016/j.measen.2023.100749.

Article  Google Scholar 

Fuse H, Oishi K, Maikusa N, Fukami T, J. A. D. N. Initiative. Detection of Alzheimer’s disease with shape analysis of MRI images. In: 2018 Joint 10th international conference on soft computing and intelligent systems (SCIS) and 19th international symposium on advanced intelligent systems (ISIS). IEEE; 2018. pp. 1031–1034. https://doi.org/10.1109/SCIS-ISIS.2018.00171.

Chitradevi D, Prabha S. Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease. Appl Soft Comput. 2020;86:105857. https://doi.org/10.1016/J.ASOC.2019.105857.

Article  Google Scholar 

Tu Y, Lin S, Qiao J, Zhuang Y, Zhang P. Alzheimer’s disease diagnosis via multimodal feature fusion. Comput Biol Med. 2022;148:105901. https://doi.org/10.1016/j.compbiomed.2022.105901.

Article  Google Scholar 

Hazarika RA, Maji AK, Sur SN, Paul BS, Kandar D. A survey on classification algorithms of brain images in Alzheimer’s disease based on feature extraction techniques. IEEE Access. 2021;9:58503–36. https://doi.org/10.1109/ACCESS.2021.3072559.

Article  Google Scholar 

Ayus I, Gupta D. A novel hybrid ensemble based Alzheimer’s identification system using deep learning technique. Biomed Signal Process Control. 2024;92:106079. https://doi.org/10.1016/j.bspc.2024.106079.

Article  Google Scholar 

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). IEEE; 2016. pp. 770–778. https://doi.org/10.1109/CVPR.2016.90.

Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324. https://doi.org/10.1109/5.726791.

Article  Google Scholar 

Hazarika RA, Abraham A, Kandar D, Maji AK. An improved LeNet-deep neural network model for Alzheimer’s disease classification using brain magnetic resonance images. IEEE Access. 2021;9:161194–207. https://doi.org/10.1109/ACCESS.2021.3131741.

Article  Google Scholar 

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 Sep 4. https://doi.org/10.48550/arXiv.1409.1556

Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199. 2013 Dec 21. https://doi.org/10.48550/arXiv.1312.6199

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2017;4700–4708. https://doi.org/10.48550/arXiv.1608.06993

ADNI | Alzheimer’s Disease Neuroimaging Initiative. [Online]. Available: https://adni.loni.usc.edu. Accessed 06 Mar 2024.

Gunawardena KANNP, Rajapakse RN, Kodikara ND. Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural MRI data. In: 2017 24th International conference on mechatronics and machine vision in practice (M2VIP). IEEE; 2017. pp. 1–7. https://doi.org/10.1109/M2VIP.2017.8211486.

Yan H, Mubonanyikuzo V, Komolafe TE, Zhou L, Wu T, Wang N. Hybrid-RViT: hybridizing ResNet-50 and vision transformer for enhanced Alzheimer’s disease detection. PLoS ONE. 2025;20(2):e0318998. https://doi.org/10.1371/JOURNAL.PONE.0318998.

Article  Google Scholar 

Maity R, et al. Early detection of Alzheimer’s disease in structural and functional MRI. Front Med. 2024. https://doi.org/10.3389/FMED.2024.1520878.

Article  Google Scholar 

Zia-Ur-Rehman, et al. Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique. PLoS One. 2024;19(9). https://doi.org/10.1371/JOURNAL.PONE.0304995.

Ebrahimi A, Luo S, for the A. Disease Neuroimaging Initiative. Convolutional neural networks for Alzheimer’s disease detection on MRI images. J Med Imaging (Bellingham). 2021;8(2). https://doi.org/10.1117/1.JMI.8.2.024503.

H. Ghaffari, H. Tavakoli, and G. P. Jahromi, “Deep transfer learning–based fully automated detection and classification of Alzheimer’s disease on brain MRI,” British Journal of Radiology, vol. 95, no. 1136, Aug. 2022, https://doi.org/10.1259/BJR.20211253/SUPPL_FILE/BJR.20211253.SUPPL-01.DOCX.

M. Sahrim, M. S. Nixon, and R. O. Carare, “Blood vessel feature description for detection of Alzheimers disease,” in 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Dec. 2014, pp. 317–322. https://doi.org/10.1109/ICARCV.2014.7064325.

Raju M, Sudila TV, Gopi VP, Anitha VS. Classification of mild cognitive impairment and Alzheimer’s disease from magnetic resonance images using deep learning. In: 2020 International conference on recent trends on electronics, information, communication & technology (RTEICT). IEEE; 2020. pp. 52–57. https://doi.org/10.1109/RTEICT49044.2020.9315695.

Comments (0)

No login
gif