Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer’s and Parkinson’s Disease Classification

Viswan Vimbi; Noushath Shaffi; Mufti Mahmud; Karthikeyan Subramanian; Faizal Hajamohideen

doi:10.1007/978-3-031-68639-9_12

Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer’s and Parkinson’s Disease Classification

Viswan Vimbi, Noushath Shaffi^*, Mufti Mahmud, Karthikeyan Subramanian, Faizal Hajamohideen

^*Corresponding author for this work

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

2 Scopus citations

Abstract

The use of transfer learning in medical imaging has shown promising results in various applications, including disease classification and segmentation. Early detection of neurological diseases like Alzheimer’s (AD) and Parkinsons (PD) is the need of the hour. This research experiments MRI datasets pertaining to AD and PD using transfer architecture of neural networks for disease classification. We used three popular datasets, namely ADNI, OASIS, and NTUA, and evaluated seven state-of-the-art transfer learning algorithms for classification. The experiments demonstrates the effectiveness of transfer learning in Alzheimer’s and Parkinson’s disease classification by achieving high accuracy and AUC scores. While the study highlights the top performing neural network models like InceptionV3 and InceptionResNetV2 for both OASIS and ADNI, it also showcase the high performances of transfer architectures like ResNet50 and EfficientNetB0 from the NTUA dataset. Additionally, we presented an ensemble of these algorithms. Relevant codes can be found at https://github.com/snoushath/AD-PD-TransferLearning.git

Original language	English
Title of host publication	Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers
Editors	Mufti Mahmud, Hanene Ben-Abdallah, M. Shamim Kaiser, Muhammad Raisuddin Ahmed, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	186-204
Number of pages	19
ISBN (Print)	9783031686382
DOIs	https://doi.org/10.1007/978-3-031-68639-9_12
State	Published - 2024
Externally published	Yes
Event	3rd International Conference on Applied Intelligence and Informatics, AII 2023 - Dubai, United Arab Emirates Duration: 29 Oct 2023 → 31 Oct 2023

Publication series

Name	Communications in Computer and Information Science
Volume	2065 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Applied Intelligence and Informatics, AII 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	29/10/23 → 31/10/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Alzheimer’s Disease
Deep Learning
Ensemble
Parkinson Disease
Transfer Learning

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-031-68639-9_12

Cite this

Vimbi, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2024). Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer’s and Parkinson’s Disease Classification. In M. Mahmud, H. Ben-Abdallah, M. S. Kaiser, M. R. Ahmed, & N. Zhong (Eds.), Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers (pp. 186-204). (Communications in Computer and Information Science; Vol. 2065 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-68639-9_12

Vimbi, Viswan ; Shaffi, Noushath ; Mahmud, Mufti et al. / Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer’s and Parkinson’s Disease Classification. Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers. editor / Mufti Mahmud ; Hanene Ben-Abdallah ; M. Shamim Kaiser ; Muhammad Raisuddin Ahmed ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2024. pp. 186-204 (Communications in Computer and Information Science).

@inproceedings{12cb4eb140ac4f41b47cb7b787931227,

title = "Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer{\textquoteright}s and Parkinson{\textquoteright}s Disease Classification",

abstract = "The use of transfer learning in medical imaging has shown promising results in various applications, including disease classification and segmentation. Early detection of neurological diseases like Alzheimer{\textquoteright}s (AD) and Parkinsons (PD) is the need of the hour. This research experiments MRI datasets pertaining to AD and PD using transfer architecture of neural networks for disease classification. We used three popular datasets, namely ADNI, OASIS, and NTUA, and evaluated seven state-of-the-art transfer learning algorithms for classification. The experiments demonstrates the effectiveness of transfer learning in Alzheimer{\textquoteright}s and Parkinson{\textquoteright}s disease classification by achieving high accuracy and AUC scores. While the study highlights the top performing neural network models like InceptionV3 and InceptionResNetV2 for both OASIS and ADNI, it also showcase the high performances of transfer architectures like ResNet50 and EfficientNetB0 from the NTUA dataset. Additionally, we presented an ensemble of these algorithms. Relevant codes can be found at https://github.com/snoushath/AD-PD-TransferLearning.git",

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Vimbi, V, Shaffi, N, Mahmud, M, Subramanian, K & Hajamohideen, F 2024, Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer’s and Parkinson’s Disease Classification. in M Mahmud, H Ben-Abdallah, MS Kaiser, MR Ahmed & N Zhong (eds), Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers. Communications in Computer and Information Science, vol. 2065 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 186-204, 3rd International Conference on Applied Intelligence and Informatics, AII 2023, Dubai, United Arab Emirates, 29/10/23. https://doi.org/10.1007/978-3-031-68639-9_12

Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer’s and Parkinson’s Disease Classification. / Vimbi, Viswan; Shaffi, Noushath; Mahmud, Mufti et al.
Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers. ed. / Mufti Mahmud; Hanene Ben-Abdallah; M. Shamim Kaiser; Muhammad Raisuddin Ahmed; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2024. p. 186-204 (Communications in Computer and Information Science; Vol. 2065 CCIS).

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

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AU - Shaffi, Noushath

AU - Mahmud, Mufti

AU - Subramanian, Karthikeyan

AU - Hajamohideen, Faizal

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AB - The use of transfer learning in medical imaging has shown promising results in various applications, including disease classification and segmentation. Early detection of neurological diseases like Alzheimer’s (AD) and Parkinsons (PD) is the need of the hour. This research experiments MRI datasets pertaining to AD and PD using transfer architecture of neural networks for disease classification. We used three popular datasets, namely ADNI, OASIS, and NTUA, and evaluated seven state-of-the-art transfer learning algorithms for classification. The experiments demonstrates the effectiveness of transfer learning in Alzheimer’s and Parkinson’s disease classification by achieving high accuracy and AUC scores. While the study highlights the top performing neural network models like InceptionV3 and InceptionResNetV2 for both OASIS and ADNI, it also showcase the high performances of transfer architectures like ResNet50 and EfficientNetB0 from the NTUA dataset. Additionally, we presented an ensemble of these algorithms. Relevant codes can be found at https://github.com/snoushath/AD-PD-TransferLearning.git

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Vimbi V, Shaffi N, Mahmud M, Subramanian K, Hajamohideen F. Transfer Learning-Based Ensemble of Deep Neural Architectures for Alzheimer’s and Parkinson’s Disease Classification. In Mahmud M, Ben-Abdallah H, Kaiser MS, Ahmed MR, Zhong N, editors, Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 186-204. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-68639-9_12