A Comparative Study of the CNN Model for AD Diagnosis

• 영문명: A Comparative Study of the CNN Model for AD Diagnosis
• 발행기관: 한국스마트미디어학회
• 저자명: Vyshnavi Ramineni Goo-Rak Kwon
• 간행물 정보: 『스마트미디어저널』Vol12, No.7, 52~58쪽, 전체 7쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2023.08.31

영문 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.

목차

Ⅱ. INTRODUCTION

Ⅲ. METHODOLOGY

Ⅳ. EXPERIMENT RESULT & DISCUSSION

Ⅴ. CONCLUSION

ACKNOWLEDGMENT

REFERENCES

키워드

Alzheimer’s disease Free surfer Convolution Neural Network MRI.