Towards low cost automated smartphone- and cloud-based otitis media diagnosis
Loading...
Date
Authors
Myburgh, Hermanus Carel
Jose, Stacy
Swanepoel, De Wet
Laurent, Claude
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
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.
Description
Keywords
Acute otitis media (AOM), Otitis media with effusion (OME), Chronic suppurative otitis media (CSOM), Tympanic membrane, Otoscope, Neural network, Decision tree, Feature extraction, Image processing, Otitis media (OM)
Sustainable Development Goals
Citation
Myburgh, H.C., Jose, S., Swanepoel, D.W. & Laurent, C. 2018, 'Towards low cost automated smartphone- and cloud-based otitis media diagnosis', Biomedical Signal Processing and Control, vol. 39, pp. 34-52.