Basaran, ErdalComert, ZaferSengur, AbdulkadirBudak, UmitCelik, YukselTogacar, Mesut2024-09-292024-09-292019978-1-7281-3964-7https://doi.org/10.1109/ubmk.2019.8907070https://hdl.handle.net/20.500.14619/61854th International Conference on Computer Science and Engineering (UBMK) -- SEP 11-15, 2019 -- Samsun, TURKEYChronic 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.eninfo:eu-repo/semantics/closedAccessBiomedical signal processingdiagnosis systemotitis mediadeep convolutional neural networkConference Object10.1109/ubmk.2019.89070702-s2.0-85076204552638N/A635WOS:000609879900120N/A