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Alzheimer’s disease is a significant neurological condition that result in memory loss and a steady deterioration in cognitive function. It primarily affects those 65 and older. Early diagnosis is crucial for efficient illness management and better patient outcomes. This paper presents a through deep learning approach that begins with the over-sampling synthetic minority method (SMOTE) to balance the Alzheimer’s Disease Neuroimaging Initiative’s (ADNI) dataset which improves the model’s ability to learn from minority classes. Following that, a 2D convolutional neural network (CNN) is used to categorize MRI images into four groups: non-demented, very mild demented, mild demented, and moderate demented. The model underwent rigorous experimentation, assessing various optimizers, dropout rates, and architectural configurations. Finally, the 4 Convo + 4 Pool layer configuration combined with a dropout rate of 0.3 and Adam optimizer achieved an impressive classification accuracy of 99.4%. These findings illustrate the effectiveness of the proposed methodology in accurately detecting Alzheimer’s disease and demonstrate its potential for clinical implementation.

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Enhanced Alzheimer’s Disease Classification Using CNN and SMOTE with Optimizer Evaluation

  • 会议论文

  • 发表日期：

    2026年

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  • Chitresh Singhal,
  • Nilesh Kumar Pandey,
  • Rekh Ram Janghel

摘要

Alzheimer’s disease is a significant neurological condition that result in memory loss and a steady deterioration in cognitive function. It primarily affects those 65 and older. Early diagnosis is crucial for efficient illness management and better patient outcomes. This paper presents a through deep learning approach that begins with the over-sampling synthetic minority method (SMOTE) to balance the Alzheimer’s Disease Neuroimaging Initiative’s (ADNI) dataset which improves the model’s ability to learn from minority classes. Following that, a 2D convolutional neural network (CNN) is used to categorize MRI images into four groups: non-demented, very mild demented, mild demented, and moderate demented. The model underwent rigorous experimentation, assessing various optimizers, dropout rates, and architectural configurations. Finally, the 4 Convo + 4 Pool layer configuration combined with a dropout rate of 0.3 and Adam optimizer achieved an impressive classification accuracy of 99.4%. These findings illustrate the effectiveness of the proposed methodology in accurately detecting Alzheimer’s disease and demonstrate its potential for clinical implementation.