Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm
Adi Alhudhaif1, Zafer Cömert2, Kemal Polat3
1Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
Background:
Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone.
Methods:
In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes.
Results:
The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.
Insights
A new AI model accurately diagnoses otitis media (OM) using ear images, achieving 98.26% accuracy. This deep learning approach offers objective results, potentially reducing misdiagnosis rates in clinical settings.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Otolaryngology
Background:
- Otitis media (OM) is a common middle ear infection diagnosed via subjective otoscope image inspection.
- Current diagnostic methods are prone to errors and subjectivity.
- There is a need for objective and reliable diagnostic tools for OM.
Purpose of the Study:
- To develop a novel computer-aided decision support model for diagnosing otitis media.
- To enhance the model's generalization ability using advanced deep learning techniques.
- To provide an objective and repeatable diagnostic aid for otitis media.
Main Methods:
- A convolutional neural network (CNN) based model was developed.
- The model incorporates channel and spatial attention (CBAM), residual blocks, and hypercolumn techniques.
- Experiments were conducted on an open-access dataset of 956 otoscope images across five classes.
Main Results:
- The proposed CNN model achieved high classification performance.
- Overall accuracy was 98.26%, with 97.68% sensitivity and 99.30% specificity.
- The model outperformed pre-trained CNNs like AlexNet, VGG-Nets, GoogLeNet, and ResNets.
Conclusions:
- The developed CNN model with integrated image processing techniques is effective for otitis media diagnosis.
- This AI tool can assist specialists in achieving objective, repeatable results and reducing misdiagnosis.
- The model supports clinical decision-making, improving the accuracy and efficiency of OM diagnosis.
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