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<p>Alzheimer's disease (AD) is a progressive neurological disorder impacting a significant segment of the global population. Magnetic resonance imaging (MRI) is used to visualize brain structures and detect changes associated with AD. Early detection remains a major challenge, particularly for multiclass classification of disease severity. This paper presents an integrated hybrid framework for AD diagnosis combining Variational Mode Decomposition, fuzzy clustering, and multi-kernel learning. The proposed method comprises: (i) Variational Mode and Non-convex Optimized pre-processing for noise removal, (ii) Fuzzy Relevance Vector Machine-based segmentation to identify regions of interest, and (iii) Multi-Kernel SVM based clustering for classifying mild, moderate, and non-demented cases. The framework is validated on the Alzheimer's Disease Multiclass Images Dataset. Performance is evaluated using peak signal-to-noise ratio (PSNR), segmentation accuracy, training time, and precision. The results demonstrate that systematic integration of these established techniques achieves competitive performance, with average PSNR of 28.45&#xa0;dB, segmentation accuracy of 91%, and precision of 90.6%.</p>

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Hybridization of fuzzy logic based relevance vector and multi kernel image enhancement for Alzheimer′s disease diagnosis

  • 期刊论文

  • 发表日期：

    2026年7月5日

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  • V. R. Dhivyabharathi,
  • S. Suresh Kumar

摘要

Alzheimer's disease (AD) is a progressive neurological disorder impacting a significant segment of the global population. Magnetic resonance imaging (MRI) is used to visualize brain structures and detect changes associated with AD. Early detection remains a major challenge, particularly for multiclass classification of disease severity. This paper presents an integrated hybrid framework for AD diagnosis combining Variational Mode Decomposition, fuzzy clustering, and multi-kernel learning. The proposed method comprises: (i) Variational Mode and Non-convex Optimized pre-processing for noise removal, (ii) Fuzzy Relevance Vector Machine-based segmentation to identify regions of interest, and (iii) Multi-Kernel SVM based clustering for classifying mild, moderate, and non-demented cases. The framework is validated on the Alzheimer's Disease Multiclass Images Dataset. Performance is evaluated using peak signal-to-noise ratio (PSNR), segmentation accuracy, training time, and precision. The results demonstrate that systematic integration of these established techniques achieves competitive performance, with average PSNR of 28.45 dB, segmentation accuracy of 91%, and precision of 90.6%.