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<p>Alzheimer’s Disease is a condition that affects the brain and causes changes in behavior and memory loss while making it hard to carry out tasks properly. It’s vital to spot the illness early, for effective treatment. MRI technology has advanced in detecting Alzheimer’s by using machine learning and deep learning models. These models use neural networks to analyze brain MRI results automatically and identify key indicators of Alzheimer’s disease. In this study, we used MRI data to train a CNN for diagnosing and categorizing the four stages of Alzheimer’s disease with deep learning techniques, offering significant advantages in identifying patterns in medical imaging for this neurodegenerative condition compared to using a CNN exclusively trained for this purpose. They evaluated ResNet50, InceptionResNetv2 as well as a CNN specifically trained for their study and found that combining the models led to highly accurate results. The accuracy rates for the trained CNN model stood at 90.76%, InceptionResNetv2 at 86.84%, and ResNet50 at 90.27%. In this trial run of the experiment conducted by combining all three models collaboratively resulted in an accuracy rate of 94.27% compared to the accuracy rates of each model working individually.</p>

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Advanced MRI based Alzheimer’s diagnosis through ensemble learning techniques

  • 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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  • Suthir Sriram,
  • V. Nivethitha,
  • T. P. Arun Kaarthic,
  • S. Archita,
  • Thangavel Murugan

摘要

Alzheimer’s Disease is a condition that affects the brain and causes changes in behavior and memory loss while making it hard to carry out tasks properly. It’s vital to spot the illness early, for effective treatment. MRI technology has advanced in detecting Alzheimer’s by using machine learning and deep learning models. These models use neural networks to analyze brain MRI results automatically and identify key indicators of Alzheimer’s disease. In this study, we used MRI data to train a CNN for diagnosing and categorizing the four stages of Alzheimer’s disease with deep learning techniques, offering significant advantages in identifying patterns in medical imaging for this neurodegenerative condition compared to using a CNN exclusively trained for this purpose. They evaluated ResNet50, InceptionResNetv2 as well as a CNN specifically trained for their study and found that combining the models led to highly accurate results. The accuracy rates for the trained CNN model stood at 90.76%, InceptionResNetv2 at 86.84%, and ResNet50 at 90.27%. In this trial run of the experiment conducted by combining all three models collaboratively resulted in an accuracy rate of 94.27% compared to the accuracy rates of each model working individually.