Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network

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dc.authoridCELIK, YUKSEL/0000-0002-7117-9736
dc.authoridComert, Zafer/0000-0001-5256-7648
dc.authoridTogacar, Mesut/0000-0002-8264-3899
dc.authoridSengur, Abdulkadir/0000-0003-1614-2639
dc.contributor.authorBasaran, Erdal
dc.contributor.authorComert, Zafer
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorBudak, Umit
dc.contributor.authorCelik, Yuksel
dc.contributor.authorTogacar, Mesut
dc.date.accessioned2024-09-29T16:04:31Z
dc.date.available2024-09-29T16:04:31Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.description4th International Conference on Computer Science and Engineering (UBMK) -- SEP 11-15, 2019 -- Samsun, TURKEYen_US
dc.description.abstractChronic Otitis Media (COM) causes deformation of the middle ear ossicles with perforation as a result of long-lasting inflammation of the middle ear and it is one of the basic reasons for hearing loss. The middle ear images are examined by otolaryngologists in the diagnosis of the disease in clinical practice. The observers make a decision considering the status of the tympanic membrane images. Decision support systems using image processing techniques and machine learning algorithms are quite useful in the diagnosis process, however, the usage of such systems in this field is limited. In this study, we propose a diagnostic model using a pretrained deep convolutional neural network (DCNN) called AlexNet. The experiments were carried out on a private dataset consisting of totally 598 tympanic membrane images collected from patients admitted to Ozel Van Akdamar Hospital. Firstly, a set of preprocessing procedures were applied to the eardrum images. Then, the tympanic membrane images were used to feed the DCNN model. The proposed model was trained using transfer learning approach. To evaluate and validate the success of the proposed model, the 10-fold cross-validation method was used. As a result, the model provided satisfactory results with an accuracy of 98.77%. Consequently, the proposed DCNN model was determined as a robust tool in separating chronic and normal tympanic membrane images.en_US
dc.description.sponsorshipIEEE,IEEE Turkey Secten_US
dc.identifier.doi10.1109/ubmk.2019.8907070
dc.identifier.endpage638en_US
dc.identifier.isbn978-1-7281-3964-7
dc.identifier.scopus2-s2.0-85076204552en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage635en_US
dc.identifier.urihttps://doi.org/10.1109/ubmk.2019.8907070
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6185
dc.identifier.wosWOS:000609879900120en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 4th International Conference On Computer Science and Engineering (Ubmk)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical signal processingen_US
dc.subjectdiagnosis systemen_US
dc.subjectotitis mediaen_US
dc.subjectdeep convolutional neural networken_US
dc.titleChronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Networken_US
dc.typeConference Objecten_US

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