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  • 1. Myburgh, Hermanus C.
    et al.
    Jose, Stacy
    Swanepoel, De Wet
    Laurent, Claude
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Otorhinolaryngology. Department of Audiology and Speech-Language Pathology, University of Pretoria, Pretoria, South Africa.
    Towards low cost automated smartphone- and cloud-based otitis media diagnosis2018In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 39, p. 34-52Article in journal (Refereed)
    Abstract [en]

    Odds 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. 

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