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

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.