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    Optimizing deep learning with improved Harris Hawks optimization for Alzheimer’s disease detection

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    ; Dordrecht Vol. 58, Iss. 10,  (Oct 2025): 301.     DOI:10.1007/s10462-025-11304-9
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    References (99)

    • 1.
      Aderghal K, Benois-Pineau J, Afdel K, Gwenaëlle C (2017) FuseMe: classification of sMRI images by fusion of deep CNNs in 2D+ ε projections. In: Proceedings of the 15th international workshop on content-based multimedia indexing
    • 2.
      Aderghal K, Boissenin M, Benois-Pineau J, Catheline G, Afdel K (2016) Classification of sMRI for AD diagnosis with convolutional neuronal networks: a pilot 2-D+ study on ADNI. Multimedia modeling, lecture notes in computer science, p 690–701
    • 3.
      Aladdin AM, Rashid TA (2023) Leo: lagrange elementary optimization. arXiv preprint arXiv:2304.05346
    • 4.
      Bochinski E, Senst T, Sikora T (2017) Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms. In: 2017 IEEE International conference on image processing (ICIP). IEEE, pp 3924–3928
    • 5.
      Dehkordi AA, Sadiq AS, Mirjalili S, Ghafoor KZ (2021) Nonlinear-based Chaotic Harris Hawks optimizer: algorithm and internet of vehicles application. Appl Soft Comput 109
    • 6.
      Heidari AA, Abbaspour RA, Chen HL (2019) Efficient boosted Grey Wolf Optimizer s for global search and kernel extreme learning machine training. Appl Soft Comput 81
    • 7.
      He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770-778
    • 8.
      Jang J, Hwang D (2022) M3T: three-dimensional Medical image classifier using multi-plane and Multi-slice Transformer. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. New Orleans, LA
    • 9.
      Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-International Conference on Neural Networks. IEEE.
    • 10.
      Kushol R, Masoumzadeh A, Huo D, Kalra S, Yang YH. Addformer: Alzheimer’s disease detection from structural Mri using fusion transformer . In: 2022 IEEE 19th international symposium on biomedical imaging (ISBI)
    • 11.
      LaTorre A, Pena JM (2017) A comparison of three large-scale global optimizers on the CEC 2017 single objective real parameter numerical optimization benchmark. In: 2017 IEEE congress on evolutionary computation, CEC 2017—proceedings
    • 12.
      Li C, Cui Y, Luo N, Liu Y, Bourgeat P, Fripp J et al (2022) Trans-ResNet: integrating transformers and CNNs for Alzheimer’s disease classification. In: 2022 IEEE 19th international symposium on biomedical imaging (ISBI)..
    • 13.
      Logan R, Williams BG, Ferreira da Silva M, Indani A, Schcolnicov N, Ganguly A, Miller SJ (2021) Deep convolutional neural networks with ensemble learning and generative adversarial networks for Alzheimer’s disease image data classification. Front Aging Neurosci 13:720226.
    • 14.
      Lu F, Ma Q, Shi C, Yue W (2025) Changes in the parietal lobe subregion volume at various stages of Alzheimer’s disease and the role in cognitively normal and mild cognitive impairment conversion. JIN 24(1)
    • 15.
      Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision
    • 16.
      Sheng J, Zhang Q, Zhang Q, Wang L, Yang Z, Xin Y, et al (2024) A hybrid multimodal machine learning model for detecting Alzheimer's disease. Comput Biol Med, p 108035
    • 17.
      Wang Y, Chen K, Wang H (2024) Adapt: Alzheimer diagnosis through adaptive profiling transformers. arXiv preprint arXiv:2401.06349
    • 18.

      A novel cnn-vit-based deep learning model for early skin cancer diagnosis

      I Pacal; B Ozdemir; J Zeynalov; H Gasimov; N Pacal. Biomed Signal Process Control Vol. 104, (2025).
    • 19.

      Multimodal mixing convolutional neural network and transformer for Alzheimer’s disease recognition

      J Chen; Y Wang; A Zeb; MD Suzauddola; Y Wen. Expert Syst Appl Vol. 259, (2025).
    • 20.

      A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases

      B Bayram; I Kunduracioglu; S Ince; I Pacal. Neuroscience Vol. 568, (2025): 76-94.