Skip to content
2000
Volume 22, Issue 9
  • ISSN: 1567-2050
  • E-ISSN: 1875-5828

Abstract

Introduction

Alzheimer's disease (AD) is a leading cause of death, making early detection critical to improve survival rates. Conventional manual techniques struggle with early diagnosis due to the brain's complex structure, necessitating the use of dependable deep learning (DL) methods. This research proposes a novel RESIGN model is a combination of Res-InceptionSeg for detecting AD utilizing MRI images.

Methods

The input MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce noise artifacts. A ResNet-LSTM model was used for feature extraction, targeting White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). The extracted features were concatenated and classified into Normal, MCI, and AD categories using an Inception V3-based classifier. Additionally, SegNet was employed for abnormal brain region segmentation.

Results

The RESIGN model achieved an accuracy of 99.46%, specificity of 98.68%, precision of 95.63%, recall of 97.10%, and an F1 score of 95.42%. It outperformed ResNet, AlexNet, DenseNet, and LSTM by 7.87%, 5.65%, 3.92%, and 1.53%, respectively, and further improved accuracy by 25.69%, 5.29%, 2.03%, and 1.71% over ResNet18, CLSTM, VGG19, and CNN, respectively.

Discussion

The integration of spatial-temporal feature extraction, hybrid classification, and deep segmentation makes RESIGN highly reliable in detecting AD. A 5-fold cross-validation proved its robustness, and its performance exceeded that of existing models on the ADNI dataset. However, there are potential limitations related to dataset bias and limited generalizability due to uniform imaging conditions.

Conclusion

The proposed RESIGN model demonstrates significant improvement in early AD detection through robust feature extraction and classification by offering a reliable tool for clinical diagnosis.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/car/10.2174/0115672050373821250612053943
2025-06-18
2025-12-30

  • From This Site

    /content/journals/car/10.2174/0115672050373821250612053943
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. AA, M.P.; Hamdi M, Bourouis S, Rastislav K, Mohmed F. Evaluation of neuro images for the diagnosis of Alzheimer’s disease using deep learning neural network.Front. Public Health20221083403210.3389/fpubh.2022.834032 35198526
     [Google Scholar]
  2. YashudasA. GuptaD. PrashantG.C. Deep-cardio: Recommendation system for cardiovascular disease prediction using IoT network.IEEE Senssors Journal20232492910.1109/JSEN.2024.3373429
     [Google Scholar]
  3. BrownJ. Alzheimer’s disease caregiver resource (AlCaRe): Tailored mobile health application. Doctoral dissertation, North Carolina Agricultural and Technical State University2020
     [Google Scholar]
  4. MartinS.S. AdayA.W. AlmarzooqZ.I. 2024 heart disease and stroke statistics: A report of US and global data from the American heart association.Circulation20241498e347e91310.1161/CIR.0000000000001209 38264914
     [Google Scholar]
  5. BrownC.S. NingX. MoneyA. Trends in cause-specific mortality among persons with Alzheimer’s disease in South Carolina: 2014 to 2019.Front. Aging Neurosci.202416138708210.3389/fnagi.2024.1387082 38694259
     [Google Scholar]
  6. CaoE. MaD. NayakS. DuongT.Q. Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer’s dementia 3-year post MCI diagnosis.Neurobiol. Dis.202318710631010.1016/j.nbd.2023.106310 37769746
     [Google Scholar]
  7. SundarasekarR. AppathuraiA. Efficient brain tumor detection and classification using magnetic resonance imaging.Biomed. Phys. Eng. Express20217505500710.1088/2057‑1976/ac0ccc 34260415
     [Google Scholar]
  8. YaoZ. WangH. YanW. Artificial intelligence-based diagnosis of Alzheimer’s disease with brain MRI images.Eur. J. Radiol.202316511093410.1016/j.ejrad.2023.110934 37354773
     [Google Scholar]
  9. AngelS.J. AhilanA. Classification of brain disease using deep learning with multi-modality images.J. Intell. Fuzzy Syst.20234523201321110.3233/JIFS‑230090
     [Google Scholar]
  10. RamzanF. KhanM.U.G. RehmatA. A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks.J. Med. Syst.20204423710.1007/s10916‑019‑1475‑2 31853655
     [Google Scholar]
  11. ChanK.Y. Abu-SalihB. QaddouraR. Deep neural networks in the cloud: Review, applications, challenges and research directions.Neurocomputing202354512632710.1016/j.neucom.2023.126327
     [Google Scholar]
  12. WangX. HuangJ. ChatzakouM. Comparison of one- two- and three-dimensional CNN models for drawing-test-based diagnostics of the Parkinson’s disease.Biomed. Signal Process. Control20248710543610.1016/j.bspc.2023.105436
     [Google Scholar]
  13. El-LatifA.A.A. ChellougS.A. AlabdulhafithM. HammadM. Accurate detection of Alzheimer’s disease using lightweight deep learning model on MRI data.Diagnostics2023137121610.3390/diagnostics13071216 37046434
     [Google Scholar]
  14. XiaZ. YueG. XuY. A novel end-to-end hybrid network for Alzheimer's disease detection using 3D CNN and 3D CLSTM.In: 2020 IEEE 17th International.IEEE 20201410.1109/ISBI45749.2020.9098621
     [Google Scholar]
  15. HussainE. HasanM. HassanS.Z. AzmiT.H. RahmanM.A. ParvezM.Z. Deep leaarning based binary classification for Alzheimer's diseases detection using brain MRI images202010.1109/ICIEA48937.2020.9248213
     [Google Scholar]
  16. OdusamiM. MaskeliūnasR. DamaševičiusR. MisraS. Explainable deep-learning-based diagnosis of Alzheimer’s disease using multimodal input fusion of PET and MRI Images.J. Med. Biol. Eng.202343329130210.1007/s40846‑023‑00801‑3
     [Google Scholar]
  17. ShuklaG.P. KumarS. PandeyS.K. AgarwalR. VarshneyN. KumarA. Diagnosis and detection of Alzheimer’s disease using learning algorithm.Big Data Mining and Analytics20236450451210.26599/BDMA.2022.9020049
     [Google Scholar]
  18. MujahidM. RehmanA. AlamT. AlamriF.S. FatiS.M. SabaT. An efficient ensemble approach for Alzheimer’s disease detection using an adaptive synthetic technique and deep learning.Diagnostics20231315248910.3390/diagnostics13152489 37568852
     [Google Scholar]
  19. Al ShehriW. Alzheimer’s disease diagnosis and classification using deep learning techniques.PeerJ Comput. Sci.20228e117710.7717/peerj‑cs.1177 37346304
     [Google Scholar]
  20. ArafaD.A. MoustafaH.E.D. AliH.A. Ali-EldinA.M.T. SarayaS.F. A deep learning framework for early diagnosis of Alzheimer’s disease on MRI images.Multimedia Tools Appl.20248323767379910.1007/s11042‑023‑15738‑7
     [Google Scholar]
  21. ChenY. WangL. DingB. Automated Alzheimer’s disease classification using deep learning models with Soft-NMS and improved ResNet50 integration.J Rad Res Appl Sci202417110078210.1016/j.jrras.2023.100782
     [Google Scholar]
/content/journals/car/10.2174/0115672050373821250612053943
Loading
RESIGN: Alzheimer's Disease Detection Using Hybrid Deep Learning based Res-Inception Seg Network
Curr Alzheimer Res 22, 698 (2025); https://doi.org/10.2174/0115672050373821250612053943
/content/journals/car/10.2174/0115672050373821250612053943
/content/journals/car/10.2174/0115672050373821250612053943
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Alzheimer's disease; clinical diagnosis; deep learning; non-local means (NLM) filter; ResNet-LSTM model; SegNet

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test