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An Implementation of Effective CNN Model for AD Detection

  • Vyshnavi Ramineni (Department of Information and Communication Engineering in Chosun University) ;
  • Goo-Rak Kwon (Chosun University)
  • Received : 2024.03.08
  • Accepted : 2024.04.22
  • Published : 2024.06.28
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Abstract

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

Keywords

Acknowledgement

This study was supported by research funds from Chosun University, 2023. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01AG024904) and DOD ADNI (Department of Defense, award number W81XWH-12-2-0012). The funding details of ADNI can be found at: http://adni.loni.usc.edu/about/funding/

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