Scholarlink

Background and purpose <p>Alzheimer’s disease, a common type of dementia, gradually steals memories and impacts daily life as brain cells deteriorate. We explored how Artificial Intelligence (AI) could help spot early signs of Alzheimer’s using MRI brain scans. </p> Materials and methods <p>Our study focused on a deep learning approach, specifically a convolutional neural network (CNN), to distinguish between Alzheimer’s, Mild Cognitive Impairment (MCI), and healthy individuals. We used two well-known datasets, OASIS and ADNI, for this work. After carefully preparing the ADNI data (which included 21,324 MRI images: 7,572 from MCI patients, 5,904 from healthy controls, and 7,848 from Alzheimer’s patients) and the OASIS data (6,400 MRI images), our model performed exceptionally well. </p> Results <p>The CNN Model achieved an accuracy of 99.67% with the ADNI images and 99.06% with the OASIS images. These encouraging results, which stand up well against other studies, show that our method can effectively analyze large amounts of data and accurately classify Alzheimer’s. </p> Conclusions <p>Our main hope is that this kind of technology can give doctors and caregivers better tools to predict and detect the disease, ultimately saving time, reducing costs, and helping those affected by Alzheimer’s.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Beyond complex architectures: a streamlined CNN pipeline for robust Alzheimer’s disease classification from brain MRI

  • 期刊论文

  • 发表日期：

    2026年7月4日

查看全文
  • Mohamed Amine Jabli,
  • Moussa Mourad

摘要

Background and purpose

Alzheimer’s disease, a common type of dementia, gradually steals memories and impacts daily life as brain cells deteriorate. We explored how Artificial Intelligence (AI) could help spot early signs of Alzheimer’s using MRI brain scans.

Materials and methods

Our study focused on a deep learning approach, specifically a convolutional neural network (CNN), to distinguish between Alzheimer’s, Mild Cognitive Impairment (MCI), and healthy individuals. We used two well-known datasets, OASIS and ADNI, for this work. After carefully preparing the ADNI data (which included 21,324 MRI images: 7,572 from MCI patients, 5,904 from healthy controls, and 7,848 from Alzheimer’s patients) and the OASIS data (6,400 MRI images), our model performed exceptionally well.

Results

The CNN Model achieved an accuracy of 99.67% with the ADNI images and 99.06% with the OASIS images. These encouraging results, which stand up well against other studies, show that our method can effectively analyze large amounts of data and accurately classify Alzheimer’s.

Conclusions

Our main hope is that this kind of technology can give doctors and caregivers better tools to predict and detect the disease, ultimately saving time, reducing costs, and helping those affected by Alzheimer’s.