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<p>Alzheimer’s is a serious neurodegenerative disease that requires early detection for effective intervention. Traditional methods often struggle with accurately identifying the early stages, such as mild cognitive impairment (MCI), due to limitations in feature extraction and classification. To address these challenges, we present an optimized hybrid deep learning model for Alzheimer’s disease detection. Our model employs the Inception v3 algorithm for initial feature extraction and the ResNet 50 algorithm for classification. Additionally, we optimize the network parameters using the Adaptive Rider Optimization (ARO) algorithm to enhance detection performance. Experimental analysis using a benchmark dementia dataset demonstrates that our model achieves superior accuracy of 96.6%, precision of 98%, recall of 97%, and F1-score of 98%, outperforming state-of-the-art techniques.</p>

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An optimized hybrid deep learning model to detect Alzheimer disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • A. Sundar Raj,
  • C. Gunasundari,
  • S. Senthilkumar,
  • S. Sivamani

摘要

Alzheimer’s is a serious neurodegenerative disease that requires early detection for effective intervention. Traditional methods often struggle with accurately identifying the early stages, such as mild cognitive impairment (MCI), due to limitations in feature extraction and classification. To address these challenges, we present an optimized hybrid deep learning model for Alzheimer’s disease detection. Our model employs the Inception v3 algorithm for initial feature extraction and the ResNet 50 algorithm for classification. Additionally, we optimize the network parameters using the Adaptive Rider Optimization (ARO) algorithm to enhance detection performance. Experimental analysis using a benchmark dementia dataset demonstrates that our model achieves superior accuracy of 96.6%, precision of 98%, recall of 97%, and F1-score of 98%, outperforming state-of-the-art techniques.