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Alzheimer’s disease (AD), a progressive neurodegenerative disorder, requires early detection for effective management. This study presents a deep learning model based on Inception-ResNetV2 to classify AD stages using MRI data from the Kaggle Alzheimer’s dataset. By leveraging multi-scale feature extraction and residual learning, the model achieves 85.95% accuracy, adeptly identifying brain changes across Non-Demented, Very Mild, Mild, and Moderate Demented stages. Data augmentation and transfer learning enhance generalization, while a larger dataset could further improve performance. Future work includes exploring Transformer-based models, computational optimization, and ensemble techniques to elevate accuracy, advancing AI-driven AD diagnostics for clinical use.

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Deep Convolutional Neural Network Model for Predicting Alzheimer’s Disease

  • 会议论文

  • 发表日期：

    2026年

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  • N. P. Saravanan,
  • R. Abishek,
  • P. Gokul,
  • M. Gowtham,
  • A. Manimaran

摘要

Alzheimer’s disease (AD), a progressive neurodegenerative disorder, requires early detection for effective management. This study presents a deep learning model based on Inception-ResNetV2 to classify AD stages using MRI data from the Kaggle Alzheimer’s dataset. By leveraging multi-scale feature extraction and residual learning, the model achieves 85.95% accuracy, adeptly identifying brain changes across Non-Demented, Very Mild, Mild, and Moderate Demented stages. Data augmentation and transfer learning enhance generalization, while a larger dataset could further improve performance. Future work includes exploring Transformer-based models, computational optimization, and ensemble techniques to elevate accuracy, advancing AI-driven AD diagnostics for clinical use.