Yunyoung Nam1, Seong Jun Choi2, Jihwan Shin1, Jinseok Lee3,*
Computer Systems Science and Engineering, Vol.46, No.2, pp. 1521-1532, 2023, DOI:10.32604/csse.2023.034192
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… More >
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