Deep Convolutional Second Generation Curvelet Transform-based MR Image for Early Detection of Alzheimer’s Disease

Takrouni Wiem, Douik Ali

2021

Abstract

Merging neuroimaging data with machine learning has an important potential for the early diagnosis of Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). The applicability of multiclass classification and the prediction to define the progress of different stages of the disease have been relatively understudied. This paper presents a short review of the deep learning history and introduces a new solution for delineating changes in each stage of AD. Our Deep Convolutional Second-Generation Curvelet Transform Network (SGCTN) is divided into both levels: The feature learning level is the first task that can combine a Second-Generation Curvelet (SGC) with autoencoder trained features. Then, for each hidden layer, a pooling is used to obtain our convolutional neural network. This network is used to learn predictive information for binary and multiclass classification. Our experiments test uses a different number of Cognitively Normal (CN), AD, early EMCI, and Later LMCI subjects from the AD Neuroimaging Initiative (ADNI). Magnetic Resonance Imaging (MRI) information modalities are considered as input. The proposed DSGCCN achieves 98.1% accuracy for delineating the early MCI from CN. Furthermore, for detecting the distinctive level of AD, a multiclass classification test realizes the global accuracy of , and it more particularly differentiates MCI and AD groups from the CN group with 96% accuracy. Compared to the state-of-the-art deep approach, our results indicate that our architecture can achieve better performance for the same databases. Model analysis based (SGC) can improve the classification performance via comparison experiments.

Paper Citation

in Harvard Style

Wiem T. and Ali D. (2021). Deep Convolutional Second Generation Curvelet Transform-based MR Image for Early Detection of Alzheimer’s Disease. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 285-292. DOI: 10.5220/0010228902850292

in Bibtex Style

@conference{visapp21,
author={Takrouni Wiem and Douik Ali},
title={Deep Convolutional Second Generation Curvelet Transform-based MR Image for Early Detection of Alzheimer’s Disease},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={285-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010228902850292},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - Deep Convolutional Second Generation Curvelet Transform-based MR Image for Early Detection of Alzheimer’s Disease
SN - 978-989-758-488-6
AU - Wiem T.
AU - Ali D.
PY - 2021
SP - 285
EP - 292
DO - 10.5220/0010228902850292
PB - SciTePress