Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks

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dc.authoridCELIK, YUKSEL/0000-0002-7117-9736
dc.authoridSengur, Abdulkadir/0000-0003-1614-2639
dc.authoridComert, Zafer/0000-0001-5256-7648
dc.authoridVelappan, Subha/0000-0002-4992-4090
dc.contributor.authorBasaran, Erdal
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorComert, Zafer
dc.contributor.authorBudak, Umit
dc.contributor.authorCelik, Yuksel
dc.contributor.authorVelappan, Subha
dc.date.accessioned2024-09-29T16:04:29Z
dc.date.available2024-09-29T16:04:29Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractOtitis Media (OM) is the general name of middle ear inflammation. In order to diagnose this disease, it is important to examine the middle ear tympanic membrane (TM) by a standard otoscopy device. In recent years, biomedical image processing and machine learning algorithms have become quite effective in diagnostic applications. To this aim, we propose a combination of gray-level co-occurrence matrix (GLCM) and artificial neural network (ANN) to distinguish acute tympanic membrane otoscope images from normal images. For the experiment, totally 223 middle ear otoscope images were collected from the volunteer patients admitted to Van Ozel Akdamar Hospital. In the experimental study, the texture features are obtained separately from R, G, and B channels and then consolidated. In addition to the texture features provided by GLCM, the average values of each channels of the otoscope images are taken into account so as to determine whether the otoscope image belongs to acute or normal class. Lastly, this feature set is applied as the input to ANN. By experimental studies, we achieved 76.14% accuracy and with this model, we achieved promising results in diagnosing normal and acute OM disease. Consequently, the texture features were found as useful to classify normal and acute OM disease.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Sci,Inonu Univ, Comp Sci Dept,Inonu Univ, Muhendisli Fakultesien_US
dc.identifier.doi10.1109/idap.2019.8875973
dc.identifier.scopus2-s2.0-85074884454en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/idap.2019.8875973
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6147
dc.identifier.wosWOS:000591781100100en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 International Conference On Artificial Intelligence and Data Processing (Idap 2019)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOtitis mediaen_US
dc.subjectbiomedical image processingen_US
dc.subjectgray level co-occurrence matrixen_US
dc.subjectartificial neural networken_US
dc.titleNormal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networksen_US
dc.typeConference Objecten_US

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