An Implementation of Effective CNN Model for AD Detection

• 영문명: An Implementation of Effective CNN Model for AD Detection
• 발행기관: 한국스마트미디어학회
• 저자명: Vyshnavi Ramineni
• 간행물 정보: 『스마트미디어저널』Vol13, No.6, 90~97쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2024.06.28

영문 초록

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.

목차

Ⅰ. INTRODUCTION

Ⅱ. MATERIAL AND METHOD

ACKNOWLEDGMENT

REFERENCES

키워드

Alzheimer's disease (AD) Magnetic Resonance Imaging (MRI) Convolution Neural Network (CNN)