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Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach.

Heba M Afify1,2, Kamel K Mohammed3,4, Aboul Ella Hassanien5,4,6

  • 1Systems and Biomedical Engineering Department, Higher Institute of Engineering in Shorouk Academy, Al Shorouk City, Cairo, Egypt. hebaaffify@yahoo.com.

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Summary

This study introduces an automated tool using convolutional neural networks (CNNs) for diagnosing ear diseases from otoscopic images. The AI model achieved high accuracy, offering a reliable method for ear disease classification.

Keywords:
Bayesian optimizationConvolutional neural networks (CNNs)Ear imagery databaseHyperparameters

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Otolaryngology

Background:

  • Clinical diagnosis of ear diseases can be limited, necessitating improved diagnostic approaches.
  • Otoscopic image processing offers a promising avenue for enhancing ear disease diagnosis.
  • Convolutional Neural Networks (CNNs) demonstrate superior accuracy in medical diagnosis compared to traditional methods.

Purpose of the Study:

  • To develop and evaluate an automated system for diagnosing ear diseases using CNNs and Bayesian hyperparameter optimization.
  • To classify otoscopic images into four categories: normal, myringosclerosis, earwax plug, and chronic otitis media (COM).
  • To compare the performance of the proposed approach against established metrics for ear disease classification.

Main Methods:

  • Utilized a CNN architecture integrated with Bayesian hyperparameter optimization for automatic diagnosis.
  • Trained the model on a database of 616 otoscopic images and validated it on 264 testing images.
  • Evaluated classification performance using accuracy, sensitivity, specificity, and positive predictive value (PPV).

Main Results:

  • Achieved a high classification accuracy of 98.10%.
  • Demonstrated excellent performance with sensitivity of 98.11%, specificity of 99.36%, and PPV of 98.10%.
  • Successfully identified optimal CNN hyperparameters for accurate and time-efficient ear disease diagnosis.

Conclusions:

  • The proposed approach, leveraging Bayesian optimization and CNNs, provides a highly accurate and dependable method for ear disease diagnosis.
  • This automated tool can significantly improve the categorization and prediction of various ear diseases.
  • The findings support the development of AI-driven tools for enhanced otolaryngological diagnostics.