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<p>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the gradual decline in cognitive functions, particularly memory and reasoning. Early detection, especially during cognitive impairment (MCI) stage, is crucial for timely intervention and management. Enhanced diagnostic methods are essential for facilitating early identification and improving patient outcomes. This study presents a robust deep learning framework for the early detection of Alzheimer’s disease. It employs transfer learning and hyperparameter-tuning of InceptionResnetV2, InceptionV3, Xception architectures to enhance feature extraction by leveraging their pre-trained capabilities. An ensemble voting mechanism has been integrated to combine predictions from different models, optimizing both accuracy and robustness. The proposed ensemble voting approach demonstrated exceptional performance, achieving 98.96% accuracy and 100% precision for predicting classes Mildly Demented and Moderately Demented. It outperformed baseline and state-of-the-art models, highlighting its potential as a reliable tool for early diagnosis and intervention.</p>

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An ensemble model based on transfer learning for the early detection of Alzheimer’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Zahra Asghari Varzaneh,
  • Seyyed Mohammad Mousavi,
  • Reza Khoshkangini,
  • Sayyed Mostafa Moosavi Khaliji

摘要

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the gradual decline in cognitive functions, particularly memory and reasoning. Early detection, especially during cognitive impairment (MCI) stage, is crucial for timely intervention and management. Enhanced diagnostic methods are essential for facilitating early identification and improving patient outcomes. This study presents a robust deep learning framework for the early detection of Alzheimer’s disease. It employs transfer learning and hyperparameter-tuning of InceptionResnetV2, InceptionV3, Xception architectures to enhance feature extraction by leveraging their pre-trained capabilities. An ensemble voting mechanism has been integrated to combine predictions from different models, optimizing both accuracy and robustness. The proposed ensemble voting approach demonstrated exceptional performance, achieving 98.96% accuracy and 100% precision for predicting classes Mildly Demented and Moderately Demented. It outperformed baseline and state-of-the-art models, highlighting its potential as a reliable tool for early diagnosis and intervention.