Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

Adi Alhudhaif; Zafer Cömert; Kemal Polat

doi:10.7717/peerj-cs.405

Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

PeerJ Comput Sci. 2021 Feb 23:7:e405. doi: 10.7717/peerj-cs.405. eCollection 2021.

Authors

Adi Alhudhaif¹, Zafer Cömert², Kemal Polat³

Affiliations

¹ Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
² Department of Software Engineering, Samsun University, Samsun, Turkey.
³ Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.

Abstract

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.

Keywords: Biomedical image processing; Convolutional neural networks; Decision support system; Deep learning; Otitis media.

Associated data

figshare/10.6084/m9.figshare.13648166.v1

Grants and funding

This publication was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.