干旱区生态环境知识资源中心(Arid): Comparable Study of Pre-trained Model on Alzheimer Disease Classification

Arid

DOI	10.1007/978-3-030-86976-2_5
	Comparable Study of Pre-trained Model on Alzheimer Disease Classification
	Odusami, Modupe; Maskeliunas, Rytis; Damasevicius, Robertas; Misra, Sanjay
通讯作者	Odusami, M (corresponding author)，Kaunas Univ Technol, Dept Multimedia Engn, Kaunas, Lithuania.
会议名称	21st International Conference on Computational Science and Its Applications (ICCSA)
会议日期	SEP 13-16, 2021
会议地点	Cagliari, ITALY
英文摘要	The Alzheimer's disease (AD) is a type of dementia that affects millions of people worldwide every year and the occurrence will continue to be on the increase. The move to diagnose people suffering from AD at an earlier stage has been a daunting problem in mental health. In recent years, the advancement of deep learning in the likes of convolutional neural networks (CNN) has made a great effort towards an early detection of AD using magnetic resonance imaging (MRI) data. However, due to the need for highly discriminative features from MR images, it is still challenging to accurately use CNNs by training from scratch for early detection of AD. This paper aims to improve the early detection of Alzheimer's disease using deep learning for neuroimaging data. We have utilized the SqueezeNet, ResNet18, AlexNet, Vgg11, DenseNet, and InceptionV3 pre-trained models to automatically classify MR images. To validate our model, we experimented with the MR images obtained from the Open Access Series of Imaging Studies (OASIS) database. The average classification accuracy derived by SqueezeNet model for training and testing was 99.38% and 82.53% for binary class and multiclass, respectively.
英文关键词	Alzheimer disease CNN Dementia MR imaging Transfer learning
来源出版物	COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT V
ISSN	0302-9743
EISSN	1611-3349
出版年	2021
卷号	12953
ISBN	978-3-030-86976-2; 978-3-030-86975-5
出版者	SPRINGER INTERNATIONAL PUBLISHING AG
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000728364200005
WOS关键词	TOMOGRAPHY
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Mathematics, Applied
WOS研究方向	Computer Science ; Mathematics
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/379066
作者单位	[Odusami, Modupe; Maskeliunas, Rytis] Kaunas Univ Technol, Dept Multimedia Engn, Kaunas, Lithuania; [Damasevicius, Robertas] Vytautas Magnus Univ, Dept Appl Informat, Kaunas, Lithuania; [Misra, Sanjay] Covenant Univ, Dept Elect & Informat Engn, Ota, Nigeria
推荐引用方式 GB/T 7714	Odusami, Modupe,Maskeliunas, Rytis,Damasevicius, Robertas,et al. Comparable Study of Pre-trained Model on Alzheimer Disease Classification[C]:SPRINGER INTERNATIONAL PUBLISHING AG,2021.