A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease

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

doi:10.1109/SSCI52147.2023.10371843

A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease

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

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

16 Scopus citations

Abstract

Early detection of neurodegenerative diseases can be challenging, where Deep Learning (DL) techniques have shown promise. Most DL techniques provide a robust and accurate classification performance. However, due to the complex architectures of the DL models, the classification results are difficult to interpret, causing challenges for their adoption in the healthcare industry. To facilitate this, the current work proposes an effective and interpretable analysis pipeline that compares the performances of pre-trained models for the early detection of Alzheimer's Disease (AD) and Parkinson's Disease (PD). The proposed pipeline allows tuning of hyperparameters, such as batch size, number of epochs, and learning rates, to achieve more robust and accurate classification. Additionally, validation of predictions using heatmaps drawn from GradCAM are also provided.

Original language	English
Title of host publication	2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1334-1339
Number of pages	6
ISBN (Electronic)	9781665430654
DOIs	https://doi.org/10.1109/SSCI52147.2023.10371843
State	Published - 2023
Externally published	Yes
Event	2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 - Mexico City, Mexico Duration: 5 Dec 2023 → 8 Dec 2023

Publication series

Name	2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

Conference

Conference	2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023
Country/Territory	Mexico
City	Mexico City
Period	5/12/23 → 8/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Alzheimer's Disease
Deep Learning
Explainability
GradCAM
Neurodegeneration
Parkinson's Disease

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Human-Computer Interaction
Decision Sciences (miscellaneous)
Safety, Risk, Reliability and Quality

Access to Document

10.1109/SSCI52147.2023.10371843

Cite this

Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2023). A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease. In 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 (pp. 1334-1339). (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI52147.2023.10371843

Viswan, Vimbi ; Shaffi, Noushath ; Mahmud, Mufti et al. / A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease. 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1334-1339 (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023).

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title = "A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease",

abstract = "Early detection of neurodegenerative diseases can be challenging, where Deep Learning (DL) techniques have shown promise. Most DL techniques provide a robust and accurate classification performance. However, due to the complex architectures of the DL models, the classification results are difficult to interpret, causing challenges for their adoption in the healthcare industry. To facilitate this, the current work proposes an effective and interpretable analysis pipeline that compares the performances of pre-trained models for the early detection of Alzheimer's Disease (AD) and Parkinson's Disease (PD). The proposed pipeline allows tuning of hyperparameters, such as batch size, number of epochs, and learning rates, to achieve more robust and accurate classification. Additionally, validation of predictions using heatmaps drawn from GradCAM are also provided.",

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author = "Vimbi Viswan and Noushath Shaffi and Mufti Mahmud and Karthikeyan Subramanian and Faizal Hajamohideen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

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doi = "10.1109/SSCI52147.2023.10371843",

language = "English",

series = "2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023",

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Viswan, V, Shaffi, N, Mahmud, M, Subramanian, K & Hajamohideen, F 2023, A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease. in 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023. 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1334-1339, 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023, Mexico City, Mexico, 5/12/23. https://doi.org/10.1109/SSCI52147.2023.10371843

A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease. / Viswan, Vimbi; Shaffi, Noushath; Mahmud, Mufti et al.
2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1334-1339 (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023).

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

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AU - Hajamohideen, Faizal

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AB - Early detection of neurodegenerative diseases can be challenging, where Deep Learning (DL) techniques have shown promise. Most DL techniques provide a robust and accurate classification performance. However, due to the complex architectures of the DL models, the classification results are difficult to interpret, causing challenges for their adoption in the healthcare industry. To facilitate this, the current work proposes an effective and interpretable analysis pipeline that compares the performances of pre-trained models for the early detection of Alzheimer's Disease (AD) and Parkinson's Disease (PD). The proposed pipeline allows tuning of hyperparameters, such as batch size, number of epochs, and learning rates, to achieve more robust and accurate classification. Additionally, validation of predictions using heatmaps drawn from GradCAM are also provided.

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Viswan V, Shaffi N, Mahmud M, Subramanian K, Hajamohideen F. A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease. In 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1334-1339. (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023). doi: 10.1109/SSCI52147.2023.10371843

A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this