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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 Convolutional neural network approach for automatic tympanic membrane detection and classification(Elsevier Sci Ltd, 2020) Basaran, Erdal; Comert, Zafer; Celik, YukselOtitis media (OM) is a term used to describe the inflammation of the middle ear. The clinical inspection of the tympanic membrane is conducted visually by experts. Visual inspection leads to limited variability among the observers and includes human-induced errors. In this study, we sought to solve these problems using a novel diagnostic model based on a faster regional convolutional neural network (Faster R-CNN) for tympanic membrane detection, and pre-trained CNNs for tympanic membrane classification. The experimental study was conducted on a new eardrum dataset. The Faster R-CNN was initially applied to the original images. The number of images in the dataset was subsequently increased using basic image augmentation techniques such as flip and rotation. We also evaluated the success of the model in the presence of various noise effects. The original and automatically extracted tympanic membrane patches were finally input separately to the CNNs. The AlexNet, VGGNets, GoogLeNet, and ResNets models were employed. This resulted in an average precision of 75.85% in the tympanic membrane detection. All CNNs in the classification produced satisfactory results, with the proposed approach achieving an accuracy of 90.48% with the VGG-16 model. This approach can potentially be used in future otological clinical decision support systems to increase the diagnostic accuracy of the physicians and reduce the overall rate of misdiagnosis. Future studies will focus on increasing the number of samples in the eardrum dataset to cover a full range of ontological conditions. This would enable us to realize a multi-class classification in OM diagnosis. (C) 2019 Elsevier Ltd. All rights reserved.Öğe Neighbourhood component analysis and deep feature-based diagnosis model for middle ear otoscope images(Springer London Ltd, 2022) Basaran, Erdal; Comert, Zafer; Celik, YukselOtitis media (OM), known as inflammation of the middle ear, is a condition especially seen in children. To carry out a definitive diagnosis of the discomfort that manifests itself with various symptoms such as pain in the ear, fever, and discharge, the eardrum in the middle ear should be examined by a specialist. In this study, a convolution neural network was used for feature extraction from middle ear otoscope images to diagnose different types of OM. These features were extracted using AlexNet, VGG-16, GoogLeNet, ResNet-50 models. The deep features extracted from these models were combined into a new deep feature vector. This feature vector consisting of 4000 deep features was examined, and the most relevant 222 deep features were selected from this large feature set by using the neighbourhood component analysis. In this case, the number of features was decreased and a more effective feature set was obtained. In the next stage of this experimental study, this new feature set was applied as the input to the support vector machine. As a result of the experimental study, an accuracy rate of 79.02% was achieved. The results point out that the use of deep features in detecting OM provides efficient results, and the proposed approach is beneficial in reducing the number of deep features as well as achieving better classification results.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]
