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Voice pathology detection using optimized convolutional neural networks and explainable artificial intelligence-based

Roohum Jegan1, R Jayagowri1

  • 1Department of Electronics and Communication Engineering, BMS College of Engineering, Bengluru, Karnataka, India.

Computer Methods in Biomechanics and Biomedical Engineering
|October 18, 2023
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Summary

This study introduces an optimized convolutional neural network (CNN) for detecting healthy and pathological voices using mel-spectrograms. The artificial bee colony (ABC) algorithm enhanced CNN accuracy, improving voice disorder detection.

Keywords:
Voice pathology detectionexplainable artificial intelligenceimage texture featuresmel-spectrogramoptimized CNN

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Signal Processing

Background:

  • Distinguishing between healthy and pathological voices is crucial for diagnosing voice disorders.
  • Traditional methods may lack the precision required for subtle voice variations.
  • Computer-aided diagnosis offers a promising avenue for objective voice assessment.

Purpose of the Study:

  • To propose a noninvasive computer-aided assessment approach for healthy and pathological voice detection.
  • To optimize a convolutional neural network (CNN) using the artificial bee colony (ABC) algorithm for enhanced voice analysis.
  • To validate the proposed method's performance on diverse voice datasets.

Main Methods:

  • Voice samples were transformed into mel-spectrogram time-frequency representations.
  • A CNN model was trained on these spectrograms for voice classification.
  • The CNN's parameters were optimized using the artificial bee colony (ABC) algorithm.
  • Gradient-weighted class activation mapping (Grad-CAM) was employed for explainability.

Main Results:

  • The ABC-optimized CNN model demonstrated improved accuracy by 1.02% compared to a conventional CNN.
  • The approach showed data-independent discriminative representation ability.
  • Evaluation was performed on SVD, AVPD, and VOICED datasets, confirming robust performance.

Conclusions:

  • The proposed noninvasive, optimized CNN approach effectively detects pathological voices.
  • The ABC optimization significantly enhances CNN performance in voice analysis.
  • Explainable AI (XAI) using Grad-CAM provides transparency in the diagnostic decisions.