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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Alzheimer Disease Classification through Transfer Learning Approach.

Noman Raza1, Asma Naseer1, Maria Tamoor2

  • 1Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan.

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|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for diagnosing Alzheimer's disease (AD) using brain MRI scans. By applying transfer learning to a customized convolutional neural network (CNN), researchers achieved high accuracy in classifying AD.

Keywords:
Alzheimer’s disease classificationconvolutional neural networkdense-netgray matter

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Alzheimer's disease (AD) is a progressive neurological disorder impacting cognitive function, with increasing prevalence in individuals over 60.
  • Accurate and early diagnosis of AD is crucial for patient management and therapeutic interventions.

Purpose of the Study:

  • To develop and evaluate a deep learning model for the segmentation and classification of Alzheimer's disease using brain MRI.
  • To investigate the efficacy of transfer learning and customized convolutional neural networks (CNNs) for AD detection.

Main Methods:

  • Utilized Magnetic Resonance Imaging (MRI) scans, focusing on segmented Gray Matter (GM) images.
  • Employed transfer learning by adapting a pre-trained deep learning model.
  • Customized a convolutional neural network (CNN) architecture for image classification.

Main Results:

  • The proposed model achieved a high classification accuracy of 97.84% for Alzheimer's disease detection.
  • Model performance was evaluated across different training epochs (10, 25, and 50).

Conclusions:

  • Transfer learning with customized CNNs offers a highly accurate approach for diagnosing Alzheimer's disease from MRI scans.
  • This method shows significant potential for improving early detection and diagnosis of AD.