Abstract
Computer-Aided Ear Diagnosis System Based on CNN-LSTM Hybrid Learning Framework for Video Otoscopy Examination
Highlights
E AR diseases are among the most frequent pathologies treated in primary care with a high percentage of nonrelevant referrals, including tympanic membrane and external auditory canal abnormalities [2], [3]
Viscaino et al.: Computer-aided Ear Diagnosis System based on convolutional neural networks (CNNs)-Long Short-Term Memory (LSTM) Hybrid Learning Framework bias [5]
We propose a computer-aided diagnosis scheme for the classification of nine ear conditions on video otoscopy
Summary
E AR diseases are among the most frequent pathologies treated in primary care with a high percentage of nonrelevant referrals, including tympanic membrane and external auditory canal abnormalities [2], [3]. The diagnosis of ear diseases is carried out by a medical interview and an otoscopic examination. Otolaryngologists and general practitioners perform otoscopic examinations daily as a part of routine care [4]. The diagnosis using common tools such as otoscopy or even otoendoscopy is susceptible to misdiagnosis due to its dependence on the technical skills and experience of the physician as well as the observer subjective. A misdiagnosis impairs the appropriate implementation of treatments and health outcomes in severe physical, psychological, financial, and legal complications. The latter means that there is an opportunity to introduce new tools to support the physician in making correct decisions and improving diagnostic accuracy
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