Open Access

ARTICLE

Diagnosis of Middle Ear Diseases Based on Convolutional Neural Network

Yunyoung Nam¹, Seong Jun Choi², Jihwan Shin¹, Jinseok Lee^3,*

1 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea
2 Department of Otolaryngology-Head and Nech Surgery, Soonchunhyang University College of Medicine, Cheonan Hospital, Cheonan, 31151, Korea
3 Department of Biomedical Engineering, Kyung Hee University, Yongin, Korea

* Corresponding Author: Jinseok Lee. Email:

Computer Systems Science and Engineering 2023, 46(2), 1521-1532. https://doi.org/10.32604/csse.2023.034192

Received 08 July 2022; Accepted 22 November 2022; Issue published 09 February 2023

Abstract

An otoscope is traditionally used to examine the eardrum and ear canal. A diagnosis of otitis media (OM) relies on the experience of clinicians. If an examiner lacks experience, the examination may be difficult and time-consuming. This paper presents an ear disease classification method using middle ear images based on a convolutional neural network (CNN). Especially the segmentation and classification networks are used to classify an otoscopic image into six classes: normal, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM), congenital cholesteatoma (CC) and traumatic perforations (TMPs). The Mask R-CNN is utilized for the segmentation network to extract the region of interest (ROI) from otoscopic images. The extracted ROIs are used as guiding features for the classification. The classification is based on transfer learning with an ensemble of two CNN classifiers: EfficientNetB0 and Inception-V3. The proposed model was trained with a 5-fold cross-validation technique. The proposed method was evaluated and achieved a classification accuracy of 97.29%.

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Keywords

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