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Alzheimer’s disease is an irreparable, degenerating brain condition affecting elderly individuals and causing dementia. This ailment is attributed to cognitive decline that impairs their independence. In the early stages, patients tend to forget recent occurrences; as the disease progresses, whole events can be forgotten. To combat this, early detection and diagnosis of Alzheimer’s has been suggested by new studies utilizing feature extraction techniques along with machine learning algorithms on voxel-based brain magnetic resonance imaging (MRI) images. Analyses of both white and gray matter have proved more efficient in predicting the disease—making it imperative to recognize it quickly. Our proposal is a model that uses brain MRI examples as inputs and outputs whether someone has Alzheimer’s disease (AD) or not. To determine whether classification architectures, VGG19 and DenseNet169, perform more effectively, we have compared them.

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Identifying Alzheimer’s Disease Through MRI Images with the Application of Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Raja Ramesh Chundru,
  • Sai Varshith Gandu,
  • Chandra Shekar Thanneru,
  • S. V. Sisir Kousthub

摘要

Alzheimer’s disease is an irreparable, degenerating brain condition affecting elderly individuals and causing dementia. This ailment is attributed to cognitive decline that impairs their independence. In the early stages, patients tend to forget recent occurrences; as the disease progresses, whole events can be forgotten. To combat this, early detection and diagnosis of Alzheimer’s has been suggested by new studies utilizing feature extraction techniques along with machine learning algorithms on voxel-based brain magnetic resonance imaging (MRI) images. Analyses of both white and gray matter have proved more efficient in predicting the disease—making it imperative to recognize it quickly. Our proposal is a model that uses brain MRI examples as inputs and outputs whether someone has Alzheimer’s disease (AD) or not. To determine whether classification architectures, VGG19 and DenseNet169, perform more effectively, we have compared them.