Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model‐agnostic explanations

Atefe Aghaei; Mohsen Ebrahimi Moghaddam; Hamed Malek

doi:10.1002/ima.22762

journal article Jun 09, 2022

Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model‐agnostic explanations

Atefe Aghaei Mohsen Ebrahimi Moghaddam

Hamed Malek

International Journal of Imaging Systems and Technology Vol. 32 No. 6 pp. 1889-1902 · Wiley

View at Publisher Save 10.1002/ima.22762

Abstract

AbstractEarly diagnosis of Alzheimer's is crucial to slow the progression of the disease. In this regard, there are many attempts to detect this disease at the early stages using AI techniques such as deep learning. We have proposed an explainable method to solve the early‐stage detection of Alzheimer's using transfer learning as a well‐known approach when there is not enough data. The employed transfer learning method is a combination of fine‐tuned ResNet‐50 and Inception‐V3 with Soft‐max and SVM classifiers using averaging. Moreover, local interpretable model‐agnostic explanations (LIME) are used to show the explainability of the proposed method. The AUC, accuracy, specificity, and sensitivity on structural MRI data of 100 MCI patients were 0.94, 87%, 92%, and 82%, respectively. Also, the LIME results were subjectively evaluated. The results showed the proposed method outperformed some related works. In addition, LIME technique make model more reliable to identify the parts involved in the patient's brain.

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Details

Published: Jun 09, 2022
Vol/Issue: 32(6)
Pages: 1889-1902
License: View

Authors

A

Atefe Aghaei

Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

M

Mohsen Ebrahimi Moghaddam

Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

H

Hamed Malek

Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Cite This Article

Atefe Aghaei, Mohsen Ebrahimi Moghaddam, Hamed Malek (2022). Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model‐agnostic explanations. International Journal of Imaging Systems and Technology, 32(6), 1889-1902. https://doi.org/10.1002/ima.22762

Interpretable ensemble deep learning model for early detection of Alzheimer's disease using local interpretable model‐agnostic explanations

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