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2000
Volume 18, Issue 9
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

With the advancement in Alzheimer's disease (AD) brain, cells start to deteriorate, which eventually creates physical dependency and mental instability that interferes with daily living. Presently, this disease is immedicable. Therefore, the only suitable treatment is early detection and prevention.

Although many studies have investigated the usefulness of deep learning in AD detection, relatively few have focused on the necessary image preprocessing processes, which are essential to any computer-aided diagnostic system. Furthermore, an optimal classification strategy that takes into account a diverse handful of prominent features is required.

Methods

This paper focuses on improving MRI-based AD detection by incorporating image enhancement approaches and deep hybrid learning into a fused framework to harness the power of multiple Deep Learning (DL) architectures and Machine Learning (ML) classifiers. The deep features extracted from three heterogeneous CNN architectures, namely, VGG16, DensetNet169, and MobileNetV1, are fused to produce a more informative and discriminative hybrid feature. Furthermore, the mRMR approach was used to optimise the acquired features, followed by classification a stack of multiple ML classifiers to predict the target class.

Results

The proposed architecture based on feature fusion strategy and ensemble learning resulted in 99.53%(Accuracy), 99.73%(Precision),99.70%(Recall), and 99.72% (F1 score). The presented model outperformed individual deep CNN architectures.

Conclusion

Lastly, we present a sobol-based sensitivity analysis that illustrates the concentration of the presented technique upon significant regions of the image and can assist medical professionals in decoding the decisions. The presented technique exeplifies the potency and constancy of categorizing Alzheimer's disease.

© 2025 Bentham Science Publishers
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2026-01-09

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References

  1. Alzheimer's Disease Fact Sheet.Available from: https://www.nia.nih.gov/health/alzheimers-disease-fact-sheet accessed on 25 Dec, 2022.
  2. 2022 Alzheimer’s disease facts and figures.Alzheimers Dement.202218470078910.1002/alz.1263835289055
     [Google Scholar]
  3. K. P.M.N. Alzheimer’s classification using dynamic ensemble of classifiers selection algorithms: A performance analysis.Biomed. Signal Process. Control20216810272910.1016/j.bspc.2021.102729
     [Google Scholar]
  4. Dementia statistics.Available from: https://www.alzint.org/about/dementia-facts-figures/dementia-statistics/ accessed on 25 Dec, 2022.
  5. Available from: https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/in depth/alzheimers-stages/art-20048448 accessed on 25 Dec, 2022.
  6. LuX. WuH. ZengY. Classification of Alzheimer’s disease in MobileNet.J. Phys. Conf. Ser.20191345404201210.1088/1742‑6596/1345/4/042012
     [Google Scholar]
  7. YangJ. PanP. SongW. HuangR. LiJ. ChenK. GongQ. ZhongJ. ShiH. ShangH. Voxelwise meta-analysis of gray matter anomalies in Alzheimer’s disease and mild cognitive impairment using anatomic likelihood estimation.J. Neurol. Sci.20123161-2212910.1016/j.jns.2012.02.01022385679
     [Google Scholar]
  8. SalihQ.A. RamliA.R. MahmudR. WirzaR. Brain white and gray matter anatomy of MRI segmentation based on tissue evaluation.MedGenMed200572116369380
     [Google Scholar]
  9. AlzubaidiL. ZhangJ. HumaidiA.J. Al-DujailiA. DuanY. Al-ShammaO. SantamaríaJ. FadhelM.A. Al-AmidieM. FarhanL. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions.J. Big Data2021815310.1186/s40537‑021‑00444‑833816053
     [Google Scholar]
  10. SinghP. MuchahariM.K. Solving multi-objective optimization problem of convolutional neural network using fast forward quantum optimization algorithm: Application in digital image classification.Adv. Eng. Softw.202317610337010.1016/j.advengsoft.2022.103370
     [Google Scholar]
  11. KibriyaH. AminR. AlshehriA.H. MasoodM. AlshamraniS.S. AlshehriA. A novel and effective brain tumor classification model using deep feature fusion and famous machine learning classifiers.Comput. Intell. Neurosci.2022202211510.1155/2022/789766935378808
     [Google Scholar]
  12. SinghP. BoseS.S. A quantum-clustering optimization method for COVID-19 CT scan image segmentation.Expert Syst. Appl.202118511563710.1016/j.eswa.2021.11563734334964
     [Google Scholar]
  13. SinghP. BoseS.S. Ambiguous D-means fusion clustering algorithm based on ambiguous set theory: Special application in clustering of CT scan images of COVID-19.Knowl. Base. Syst.202123110743210.1016/j.knosys.2021.10743234462624
     [Google Scholar]
  14. ChutaniG. BohraH. DiwanD. GargN. Improved alzheimer detection using image enhancement techniques and transfer learning. 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, 27-29 May 2022, pp. 1-6.10.1109/INCET54531.2022.9824008
     [Google Scholar]
  15. LiangS. GuY. Computer-aided diagnosis of alzheimer’s disease through weak supervision deep learning framework with attention mechanism.Sensors202021122010.3390/s2101022033396415
     [Google Scholar]
  16. AlshammariM. MezherM. A modified convolutional neural networks for mri-based images for detection and stage classification of alzheimer disease.2021 National Computing Colleges Conference (NCCC), Taif, Saudi Arabia, 27-28 March 2021, pp. 1-7.10.1109/NCCC49330.2021.9428810
     [Google Scholar]
  17. AfzalS. MaqsoodM. NazirF. KhanU. AadilF. AwanK.M. MehmoodI. SongO.Y. A data augmentation-based framework to handle class imbalance problem for alzheimer’s stage detection.IEEE Access2019711552811553910.1109/ACCESS.2019.2932786
     [Google Scholar]
  18. NawazH. MaqsoodM. AfzalS. AadilF. MehmoodI. RhoS. A deep feature-based real-time system for Alzheimer disease stage detection.Multimedia Tools Appl.20218028-29357893580710.1007/s11042‑020‑09087‑y
     [Google Scholar]
  19. VaithinathanK. ParthibanL. Alzheimer’s Disease Neuroimaging Initiative A novel texture extraction technique with T1 weighted MRI for the classification of alzheimer’s disease.J. Neurosci. Methods2019318849910.1016/j.jneumeth.2019.01.01130735784
     [Google Scholar]
  20. WangY. YangY. GuoX. YeC. GaoN. FangY. MaH.T. A novel multimodal MRI analysis for alzheimer’s disease based on convolutional neural network.2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18-21 July 2018, pp. 754-757.10.1109/EMBC.2018.8512372
     [Google Scholar]
  21. LiuM. LiF. YanH. WangK. MaY. ShenL. XuM. Alzheimer’s Disease Neuroimaging Initiative A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease.Neuroimage202020811645910.1016/j.neuroimage.2019.11645931837471
     [Google Scholar]
  22. MuruganS. VenkatesanC. SumithraM.G. GaoX.Z. ElakkiyaB. AkilaM. ManoharanS. DEMNET: A deep learning model for early diagnosis of alzheimer diseases and dementia from MR images.IEEE Access20219903199032910.1109/ACCESS.2021.3090474
     [Google Scholar]
  23. AjagbeS.A. AmudaK.A. OladipupoM.A. AfeO.F. OkesolaK.I. Multi-classification of alzheimer disease on magnetic resonance images (MRI) using deep convolutional neural network (DCNN) approaches.Int. J. Adv. Comput. Sci. Res.20211153516010.19101/IJACR.2021.1152001
     [Google Scholar]
  24. SavaşS. Detecting the stages of alzheimer’s disease with pre-trained deep learning architectures.Arab. J. Sci. Eng.20224722201221810.1007/s13369‑021‑06131‑3
     [Google Scholar]
  25. ParisS. KornprobstP. TumblinJ. DurandF. Bilateral filtering: Theory and applications.Found. Trends Comput. Graph. Vis.20084117510.1561/0600000020
     [Google Scholar]
  26. UllahZ. FarooqM.U. LeeS-H. AnD A hybrid image enhancement based brain S. Sarraf, G. Tofighi, Classification of alzheimer’s disease using fMRI data and deep learning convolutional neural networksarXiv: 1603.08631, 2016
     [Google Scholar]
  27. FareedM.M.S. ZikriaS. AhmedG. Mui-Zzud-DinS. MahmoodS. AslamM. JillaniS.F. MoustafaA. AsadM. ADD-Net: An effective deep learning model for early detection of alzheimer disease in MRI scans.IEEE Access202210969309695110.1109/ACCESS.2022.3204395
     [Google Scholar]
  28. KhanN.M. AbrahamN. HonM. Transfer learning with intelligent training data selection for prediction of alzheimer’s disease.IEEE Access20197727267273510.1109/ACCESS.2019.2920448
     [Google Scholar]
  29. UllahZ. FarooqM.U. LeeS.H. AnD. A hybrid image enhancement based brain MRI images classification technique.Med. Hypotheses202014310992210.1016/j.mehy.2020.10992232682214
     [Google Scholar]
  30. SomasundaramK.G. KalavathiP. Medical image contrast enhancement based on gamma correction.Int. J. Knowl. Manag. E-Learn.201131518
     [Google Scholar]
  31. SinghD. SinghB. Investigating the impact of data normalization on classification performance.Appl. Soft Comput.20209710552410.1016/j.asoc.2019.105524
     [Google Scholar]
  32. MumuniA. MumuniF. Data augmentation: A comprehensive survey of modern approaches.Array20221610025810.1016/j.array.2022.100258
     [Google Scholar]
  33. BaldiP. SadowskiP. The dropout learning algorithm.Artif. Intell.20142107812210.1016/j.artint.2014.02.00424771879
     [Google Scholar]
  34. Abedinzadeh TorghabehF. ModaresniaY. KhalilzadehM.M. Effectiveness of Learning Rate in Dementia Severity Prediction Using VGG16SSRN202210.2139/ssrn.4245842
     [Google Scholar]
  35. TechaC. RidouaniM. HassouniL. AnounH. Alzheimer’s disease multi-class classification model based on CNN and stacknet using brain MRI data.Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022, Springer, Cham. 18 November 2022, pp 248–259.10.1007/978‑3‑031‑20601‑6_23
     [Google Scholar]
  36. SharenH. DhanushB. RukmaniP. DhanyaD. Efficient diagnosis of alzheimer’s disease using efficientnet in neuroimaging.Advanced Computing and Intelligent Technologies. SpringerSingapore202291421122310.1007/978‑981‑19‑2980‑9_18
     [Google Scholar]
  37. C RN.Jr M MK.Sr Ensemble approach for early detection and classification of stages of Alzheimer’s disease.Alzheimers Dement.202218S1e05912410.1002/alz.059124
     [Google Scholar]
  38. HowardA.G. ZhuM. ChenB. KalenichenkoD. WangW. WeyandT. AndreettoM. AdamH MobileNets: Efficient convolutional neural networks for mobile vision applications.Comput. Vis. Pattern Recognit.2017
     [Google Scholar]
  39. HuangG. LiuZ. Van Der MaatenL. WeinbergerK.Q. Densely connected convolutional networks.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21-26 July 2017, pp. 2261-2269.10.1109/CVPR.2017.243
     [Google Scholar]
  40. ZhaoZ. AnandR. WangM. Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform.2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Washington, DC, USA, 05-08 October 2019, pp. 442-452.10.1109/DSAA.2019.00059
     [Google Scholar]
  41. BoserB.E. GuyonI.M. VapnikV.N. A training algorithm for optimal margin classifiers.Proceedings of the fifth annual workshop on Computational learning theory (COLT '92).New York, NY, USAACM199214415210.1145/130385.130401
     [Google Scholar]
  42. AlexandropoulosS-A.N. AridasC.K. KotsiantisS.B. VrahatisM.N. Stacking strong ensembles of classifiers.Artificial Intelligence Applications and InnovationsSpringerCham201954555610.1007/978‑3‑030‑19823‑7_46
     [Google Scholar]
  43. HuangG.B. ZhuQ.Y. SiewC.K. Extreme learning machine: Theory and applications.Neurocomputing2006701-348950110.1016/j.neucom.2005.12.126
     [Google Scholar]
  44. WuT. ZhangW. JiaoX. GuoW. HamoudY.A. Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables.PLoS One2020156e023532410.1371/journal.pone.023532432598399
     [Google Scholar]
  45. HarikaS. YaminiT. NagasaikameshT. BashaS.H. KumarS.S. DurgaKameswariM.S.S. Alzheimers disease detection using different machine learning algorithms.Int. J. Res. Appl. Sci. Eng. Technol.20221010626610.22214/ijraset.2022.46937
     [Google Scholar]
  46. DubeyS. Alzheimer’s dataset.2022Available from: https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images Accessed on25 November 2022.
  47. ShashoaN.A.A. SalemN.A. JletaI.N. AbusaeedaO. Classification depend on linear discriminant analysis using desired outputs.2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Sousse, Tunisia, 19-21 December 2016, pp. 328-332.10.1109/STA.2016.7952041
     [Google Scholar]
  48. SharmaP. SharmaS. GoyalA. An MSE (mean square error) based analysis of deconvolution techniques used for deblurring/restoration of MRI and CT Images.Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (ICTCS '16), New York, NY, USA, March 2016: pp. 1–5.10.1145/2905055.2905257
     [Google Scholar]
  49. FelT. CadeneR. ChalvidalM. CordM. VigourouxD. SerreT. Look at the variance! Efficient black-box explanations with sobol-based sensitivity analysis.Compur Vis. Pattern Recognit.2021
     [Google Scholar]
  50. RajuM. ThirupalaniM. VidhyabharathiS. ThilagavathiS. Deep learning based multilevel classification of alzheimer’s disease using MRI scans.IOP Conf. Ser. Mater. Sci. Eng.2021108401201710.1088/1757‑899X/1084/1/012017
     [Google Scholar]
  51. DhimanPoonam A novel deep learning model for detection of severity level of the disease in citrus fruits.Electronics202211349510.3390/electronics11030495
     [Google Scholar]
  52. VermaKanupriya Latest tools for data mining and machine learning.Int. J. Innov. Technol. Explor. Eng.201989S1823
     [Google Scholar]
  53. SinghP. HuangY.P. A new hybrid time series forecasting model based on the neutrosophic set and quantum optimization algorithm.Comput. Ind.201911112113910.1016/j.compind.2019.06.004
     [Google Scholar]
  54. SinghP. FQTSFM: A fuzzy-quantum time series forecasting model.Inf. Sci.2021566577910.1016/j.ins.2021.02.024
     [Google Scholar]
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Intelligent Data Processing for Alzheimer's Disease Using Deep Learning
Recent Advances in Electrical & Electronic Engineering 18, 1708 (2025); https://doi.org/10.2174/0123520965327853240724113549
/content/journals/raeeng/10.2174/0123520965327853240724113549
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  • Article Type:     Research Article
Keyword(s): Alzheimer detection; convolution neural networks; deep hybrid learning; ensemble classifier; image enhancement; MRI

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