Otitis media is one of the most common childhood illnesses. Access to ear specialists and specialist equipment is rudimentary in many third world countries, and general practitioners do not always have enough experience in diagnosing the different otitis medias. In this paper a system recently proposed by three of the authors for automated diagnosis of middle ear pathology, or otitis media, is extended to enable the use of the system on a smartphone with an Internet connection. In addition, a neural network is also proposed in the current system as a classifier, and compared to a decision tree similar to what was proposed before. The system is able to diagnose with high accuracy (1) a normal tympanic membrane, (2) obstructing wax or foreign bodies in the external ear canal (W/O), (3) acute otitis media (AOM), (4) otitis media with effusion (OME) and (5) chronic suppurative otitis media (CSOM). The average classification accuracy of the proposed system is 81.58% (decision tree) and 86.84% (neural network) for images captured with commercial video-otoscopes, using 80% of the 389 images for training, and 20% for testing and validation.
Els, Carla(Allergy Society of South Africa, 2014-03)
During medical training all doctors are taught the importance of the cytochrome P450 (CYP) system and the
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Biagio, Leigh; Swanepoel, De Wet; Laurent, Claude; Lundberg, Thorbjörn(Health and Medical Publishing Group, 2014-06)
BACKGROUND. No published studies on the prevalence of paediatric otitis media at primary healthcare clinics (PHCs) in South Africa (SA)
OBJECTIVE. To examine the point prevalence of otitis media in a ...
Children younger than 7 years are at increased risk of otitis media because of their immature immune systems and poorly functioning eustachian tubes that normally ventilate the middle ear space and equalize pressure with ...