스마트미디어저널 (Smart Media Journal)

  • 제12권7호
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  • Pages.52-58
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  • 2023
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  • 2287-1322(pISSN)
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  • 2288-9671(eISSN)
한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
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A Comparative Study of the CNN Model for AD Diagnosis

  • Vyshnavi Ramineni (department of Information and Communication Engineering in Chosun University) ;
  • Goo-Rak Kwon (Department of Mechatronic Engineering, Korea University)
  • 투고 : 2023.07.03
  • 심사 : 2023.08.28
  • 발행 : 2023.08.31
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초록

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

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과제정보

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 U01 AG024904) 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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