Original scientific paper

https://doi.org/10.17559/TV-20240118001270

Grad-CAM-Based Feature Selection and Dementia Classification Algorithm Using Voice Data

Hansol Ko ; Gachon University, South Korea
Bohyun Wang ; Gachon University, South Korea
Joon S. Lim orcid.org/0000-0002-0018-946X ; Gachon University, South Korea *

* Corresponding author.

Abstract

This study presents a unique methodology for dementia classification that harnesses voice data and integrates transfer learning, feature selection, and attention-based visualization. We examined two deep learning input techniques: one consolidating three Melspectrograms (standard, Harmonic/Percussive average, and delta value) into an integrated image and the other assessing them individually.This study validates the efficacy of melt spectrogram image classification using Gradient-weighted Class Activation Mapping (Grad-CAM) for feature selection. This study exploited the Grad-CAM attention map to pinpoint the Melspectrogram's most impactful features. Evaluations illustrated that the combined synthetic images yielded 1.4% - 9.4% better accuracy than the separate images. Implementing Grad-CAM for feature selection further amplifies accuracy. Models utilizing features identified by Grad-CAM averaged a 4.3% superior accuracy compared with solely fine-tuned models. With integrated mel-spectrograms as input, the classification accuracies for Normal vs. Dementia, Normal vs. Mild Cognitive Impairment, and Dementia vs. Mild Cognitive Impairment were 75%, 67.9%, and 67.8%, respectively, indicating an improvement of up to 13.1% compared to individual images.

Keywords

attention-based visualization; deep learning; dementia classification; feature selection; Grad-CAM; mel-spectrogram; transfer learning; voice data

Hrčak ID:

321927

URI

https://hrcak.srce.hr/321927

Publication date:

31.10.2024.

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