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Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture.

Kamel K Mohammed1,2, Aboul Ella Hassanien3,2, Heba M Afify4,5

  • 1Center for Virus Research and Studies, Al Azhar University, Cairo, Egypt.

Journal of Digital Imaging
|March 17, 2022
PubMed
Summary

This study introduces an advanced ear diagnosis method using convolutional neural networks (CNN) and long short-term memory (LSTM) with Bayesian optimization. The approach achieves perfect accuracy in classifying ear conditions from otoscopic images.

Keywords:
Bayesian OptimizationConvolutional neural networks (CNN)Ear imagery databaseHyperparametersLong short-term memory (LSTM)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Otolaryngology

Background:

  • Clinical diagnosis of ear conditions faces accuracy limitations due to subjective expertise, image quality, and complex lesion identification.
  • There is a significant need for automated, objective diagnostic algorithms for ear pathologies using otoscopic image analysis.

Purpose of the Study:

  • To develop and evaluate an improved ear diagnosis system leveraging deep learning for enhanced accuracy.
  • To optimize the performance of a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) model through Bayesian hyperparameter tuning.

Main Methods:

  • A hybrid CNN-LSTM model was employed for feature extraction and classification of ear conditions.
  • Bayesian optimization was utilized to fine-tune the hyperparameters of the LSTM classifier.
  • The model was trained and tested on a dataset of 880 otoscopic images across four categories: normal, myringosclerosis, earwax plug, and chronic otitis media (COM).

Main Results:

  • The proposed CNN-LSTM model achieved 100% accuracy, sensitivity, specificity, and positive predictive value (PPV) on the testing dataset.
  • The study demonstrated that CNN-LSTM offers superior performance and reduced training time compared to CNN alone.
  • Bayesian optimization effectively identified optimal hyperparameters, enhancing diagnostic reliability.

Conclusions:

  • The developed CNN-LSTM approach with Bayesian optimization provides a highly accurate and reliable method for diagnosing ear conditions.
  • This automated tool has the potential to significantly improve the classification and prediction of various ear pathologies.
  • The findings highlight the efficacy of deep learning in medical image analysis for otolaryngology applications.