Early Diagnosis of Alzheimer's Disease: A Neuroimaging Study With Deep Learning Architectures

Jyoti Islam, Yanqing Zhang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 1881-1883

Abstract


Alzheimer's Disease is an incurable, progressive neurological brain disorder. Early diagnosis of Alzheimer's Disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's Disease diagnosis. Detection of Alzheimer's Disease is exacting due to the similarity in Alzheimer's Disease Magnetic Resonance Imaging (MRI) data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer's Disease diagnosis using brain MRI data analysis. We have conducted ample experiments to demonstrate that our proposed model outperforms comparative baselines on the Open Access Series of Imaging Studies (OASIS) dataset.

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[bibtex]
@InProceedings{Islam_2018_CVPR_Workshops,
author = {Islam, Jyoti and Zhang, Yanqing},
title = {Early Diagnosis of Alzheimer's Disease: A Neuroimaging Study With Deep Learning Architectures},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}