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Öğe Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network(Ieee, 2019) Basaran, Erdal; Comert, Zafer; Sengur, Abdulkadir; Budak, Umit; Celik, Yuksel; Togacar, MesutChronic 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.Öğe Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks(Ieee, 2019) Basaran, Erdal; Sengur, Abdulkadir; Comert, Zafer; Budak, Umit; Celik, Yuksel; Velappan, SubhaOtitis 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.Öğe Otitis media diagnosis model for tympanic membrane images processed in two-stage processing blocks(Iop Publishing Ltd, 2020) Basaran, Erdal; Comert, Zafer; Celik, Yuksel; Budak, Umit; Sengur, Abdulkasdir[No abstract available]
