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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Deep learning for otitis media classification using otoscopic image.

Qingqing Guo¹, Liangzhen Xie², Ling Zhou²

¹Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.

|December 2, 2025

Summary

This summary is machine-generated.

Deep learning models accurately classify otitis media (OM). VGGNet-19 achieved 94.51% accuracy, showing potential for automated diagnosis of this common ear infection.

Keywords:

convolutional neural networks deep learning model evaluation otitis media diagnosis otoscopic image classification otoscopic image dataset

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Area of Science:

Medical Imaging
Artificial Intelligence
Otolaryngology

Background:

Otitis media (OM) is a widespread health concern impacting hearing and general well-being.
Current diagnostic methods for OM rely on subjective otoscopic image interpretation, leading to diagnostic inconsistencies and errors.

Purpose of the Study:

To evaluate the efficacy of five deep learning models in classifying otoscopic images for otitis media diagnosis.
To identify the most effective deep learning model for differentiating between normal ears and various types of otitis media.

Main Methods:

A dataset of 819 otoscopic images was utilized, categorized into normal, acute otitis media, otitis media with effusion, and chronic suppurative otitis media.
Five deep learning models (ResNet-18, GoogLeNet, AlexNet, MobileNet-V3, VGGNet-19) were trained and validated on 60% and 20% of the data, respectively.
Model performance was rigorously assessed using accuracy, sensitivity, specificity, precision, F1 score, and ROC-AUC analysis on a 20% test set.

Main Results:

VGGNet-19 exhibited superior performance, achieving the highest accuracy (94.51%), sensitivity (94.18%), specificity (98.10%), and precision (93.86%).
The VGGNet-19 model demonstrated exceptional capability in identifying chronic suppurative otitis media.
While other evaluated models provided competitive results, VGGNet-19 consistently outperformed them across key performance metrics.

Conclusions:

VGGNet-19 shows significant potential as an accurate and automated tool for the classification of otitis media.
The findings support the integration of deep learning for improved diagnostic accuracy in otitis media.
Further research is recommended to address dataset imbalances and validate these findings in diverse clinical settings to enhance generalizability.