3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease

Juan Ruiz; Mufti Mahmud; Md Modasshir; M. Shamim Kaiser

doi:10.1007/978-3-030-59277-6_8

3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease

Juan Ruiz
, Mufti Mahmud^*
, Md Modasshir
, M. Shamim Kaiser

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

97 Scopus citations

Abstract

One of the major causes of death in developing nations is the Alzheimer’s Disease (AD). For the treatment of this illness, is crucial to early diagnose mild cognitive impairment (MCI) and AD, with the help of feature extraction from magnetic resonance images (MRI). This paper proposes a 4-way classification of 3D MRI images using an ensemble implementation of 3D Densely Connected Convolutional Networks (3D DenseNets) models. The research makes use of dense connections that improve the movement of data within the model, due to having each layer linked with all the subsequent layers in a block. Afterwards, a probability-based fusion method is employed to merge the probabilistic output of each unique individual classifier model. Available through the ADNI dataset, preprocessed 3D MR images from four subject groups (i.e., AD, healthy control, early MCI, and late MCI) were acquired to perform experiments. In the tests, the proposed approach yields better results than other state-of-the-art methods dealing with 3D MR images.

Original language	English
Title of host publication	Brain Informatics - 13th International Conference, BI 2020, Proceedings
Editors	Mufti Mahmud, Stefano Vassanelli, M. Shamim Kaiser, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	85-96
Number of pages	12
ISBN (Print)	9783030592769
DOIs	https://doi.org/10.1007/978-3-030-59277-6_8
State	Published - 2020
Externally published	Yes
Event	13th International Conference on Brain Informatics, BI 2020 - Padua, Italy Duration: 19 Sep 2020 → 19 Sep 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12241 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Brain Informatics, BI 2020
Country/Territory	Italy
City	Padua
Period	19/09/20 → 19/09/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Convolutional neural network
Deep learning
Machine learning
Magnetic resonance imaging
Neuroimaging

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-59277-6_8

Cite this

Ruiz, J., Mahmud, M., Modasshir, M., & Shamim Kaiser, M. (2020). 3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease. In M. Mahmud, S. Vassanelli, M. S. Kaiser, & N. Zhong (Eds.), Brain Informatics - 13th International Conference, BI 2020, Proceedings (pp. 85-96). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12241 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59277-6_8

Ruiz, Juan ; Mahmud, Mufti ; Modasshir, Md et al. / 3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease. Brain Informatics - 13th International Conference, BI 2020, Proceedings. editor / Mufti Mahmud ; Stefano Vassanelli ; M. Shamim Kaiser ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2020. pp. 85-96 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3d543dbe41164f4796551f6f9ee587f7,

title = "3D DenseNet Ensemble in 4-Way Classification of Alzheimer{\textquoteright}s Disease",

abstract = "One of the major causes of death in developing nations is the Alzheimer{\textquoteright}s Disease (AD). For the treatment of this illness, is crucial to early diagnose mild cognitive impairment (MCI) and AD, with the help of feature extraction from magnetic resonance images (MRI). This paper proposes a 4-way classification of 3D MRI images using an ensemble implementation of 3D Densely Connected Convolutional Networks (3D DenseNets) models. The research makes use of dense connections that improve the movement of data within the model, due to having each layer linked with all the subsequent layers in a block. Afterwards, a probability-based fusion method is employed to merge the probabilistic output of each unique individual classifier model. Available through the ADNI dataset, preprocessed 3D MR images from four subject groups (i.e., AD, healthy control, early MCI, and late MCI) were acquired to perform experiments. In the tests, the proposed approach yields better results than other state-of-the-art methods dealing with 3D MR images.",

keywords = "Convolutional neural network, Deep learning, Machine learning, Magnetic resonance imaging, Neuroimaging",

author = "Juan Ruiz and Mufti Mahmud and Md Modasshir and \{Shamim Kaiser\}, M.",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 13th International Conference on Brain Informatics, BI 2020 ; Conference date: 19-09-2020 Through 19-09-2020",

year = "2020",

doi = "10.1007/978-3-030-59277-6\_8",

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Ruiz, J, Mahmud, M, Modasshir, M & Shamim Kaiser, M 2020, 3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease. in M Mahmud, S Vassanelli, MS Kaiser & N Zhong (eds), Brain Informatics - 13th International Conference, BI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12241 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 85-96, 13th International Conference on Brain Informatics, BI 2020, Padua, Italy, 19/09/20. https://doi.org/10.1007/978-3-030-59277-6_8

3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease. / Ruiz, Juan; Mahmud, Mufti; Modasshir, Md et al.
Brain Informatics - 13th International Conference, BI 2020, Proceedings. ed. / Mufti Mahmud; Stefano Vassanelli; M. Shamim Kaiser; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2020. p. 85-96 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12241 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - 3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease

AU - Ruiz, Juan

AU - Mahmud, Mufti

AU - Modasshir, Md

AU - Shamim Kaiser, M.

PY - 2020

Y1 - 2020

N2 - One of the major causes of death in developing nations is the Alzheimer’s Disease (AD). For the treatment of this illness, is crucial to early diagnose mild cognitive impairment (MCI) and AD, with the help of feature extraction from magnetic resonance images (MRI). This paper proposes a 4-way classification of 3D MRI images using an ensemble implementation of 3D Densely Connected Convolutional Networks (3D DenseNets) models. The research makes use of dense connections that improve the movement of data within the model, due to having each layer linked with all the subsequent layers in a block. Afterwards, a probability-based fusion method is employed to merge the probabilistic output of each unique individual classifier model. Available through the ADNI dataset, preprocessed 3D MR images from four subject groups (i.e., AD, healthy control, early MCI, and late MCI) were acquired to perform experiments. In the tests, the proposed approach yields better results than other state-of-the-art methods dealing with 3D MR images.

AB - One of the major causes of death in developing nations is the Alzheimer’s Disease (AD). For the treatment of this illness, is crucial to early diagnose mild cognitive impairment (MCI) and AD, with the help of feature extraction from magnetic resonance images (MRI). This paper proposes a 4-way classification of 3D MRI images using an ensemble implementation of 3D Densely Connected Convolutional Networks (3D DenseNets) models. The research makes use of dense connections that improve the movement of data within the model, due to having each layer linked with all the subsequent layers in a block. Afterwards, a probability-based fusion method is employed to merge the probabilistic output of each unique individual classifier model. Available through the ADNI dataset, preprocessed 3D MR images from four subject groups (i.e., AD, healthy control, early MCI, and late MCI) were acquired to perform experiments. In the tests, the proposed approach yields better results than other state-of-the-art methods dealing with 3D MR images.

KW - Convolutional neural network

KW - Deep learning

KW - Machine learning

KW - Magnetic resonance imaging

KW - Neuroimaging

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DO - 10.1007/978-3-030-59277-6_8

M3 - Conference contribution

AN - SCOPUS:85092139475

SN - 9783030592769

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 85

EP - 96

BT - Brain Informatics - 13th International Conference, BI 2020, Proceedings

A2 - Mahmud, Mufti

A2 - Vassanelli, Stefano

A2 - Kaiser, M. Shamim

A2 - Zhong, Ning

PB - Springer Science and Business Media Deutschland GmbH

T2 - 13th International Conference on Brain Informatics, BI 2020

Y2 - 19 September 2020 through 19 September 2020

ER -

Ruiz J, Mahmud M, Modasshir M, Shamim Kaiser M. 3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease. In Mahmud M, Vassanelli S, Kaiser MS, Zhong N, editors, Brain Informatics - 13th International Conference, BI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 85-96. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59277-6_8

3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this