Browsing by Author "Uçar, M."

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An ensemble approach for classification of tympanic membrane conditions using soft voting classifier
(2024.01.01) Akyol, K.; Uçar, E.; Atila, U.; Uçar, M.
Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices utilized by field experts to diagnose the disease relevant to otitis media prevent the widespread use of these devices. This makes it difficult for field experts to make an accurate diagnosis and increases subjectivity in diagnosing the disease. To solve these problems, there is a need to develop computer-aided middle ear disease diagnosis systems. In this study, a deep learning-based approach is proposed for the detection of OM disease to meet this emerging need. This approach is the first that addresses the performance of a voting ensemble framework that uses Inception V3, DenseNet 121, VGG16, MobileNet, and EfficientNet B0 pre-trained DL models. All pre-trained CNN models used in the proposed approach were trained using the Public Ear Imagery dataset, which has a total of 880 otoscopy images, including different eardrum cases such as normal, earwax plug, myringosclerosis, and chronic otitis media. The prediction results of these models were evaluated with voting approaches to increase the overall prediction accuracy. In this context, the performances of both soft and hard voting ensembles were examined. Soft voting ensemble framework achieved highest performance in experiments with 98.8% accuracy, 97.5% sensitivity, and 99.1% specificity. Our proposed model achieved the highest classification performance so far in the current dataset. The results reveal that our voting ensemble-based DL approach showed quite high performance for the diagnosis of middle ear disease. In clinical applications, this approach can provide a preliminary diagnosis of the patient's condition just before field experts make a diagnosis on otoscopic images. Thus, our proposed approach can help field experts to diagnose the disease quickly and accurately. In this way, clinicians can make the final diagnosis by integrating automatic diagnostic prediction with their experience.
An ensemble approach for classification of tympanic membrane conditions using soft voting classifier
(Springer, 2024) Akyol, K.; Uçar, E.; Atila, Ü.; Uçar, M.
Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices utilized by field experts to diagnose the disease relevant to otitis media prevent the widespread use of these devices. This makes it difficult for field experts to make an accurate diagnosis and increases subjectivity in diagnosing the disease. To solve these problems, there is a need to develop computer-aided middle ear disease diagnosis systems. In this study, a deep learning-based approach is proposed for the detection of OM disease to meet this emerging need. This approach is the first that addresses the performance of a voting ensemble framework that uses Inception V3, DenseNet 121, VGG16, MobileNet, and EfficientNet B0 pre-trained DL models. All pre-trained CNN models used in the proposed approach were trained using the Public Ear Imagery dataset, which has a total of 880 otoscopy images, including different eardrum cases such as normal, earwax plug, myringosclerosis, and chronic otitis media. The prediction results of these models were evaluated with voting approaches to increase the overall prediction accuracy. In this context, the performances of both soft and hard voting ensembles were examined. Soft voting ensemble framework achieved highest performance in experiments with 98.8% accuracy, 97.5% sensitivity, and 99.1% specificity. Our proposed model achieved the highest classification performance so far in the current dataset. The results reveal that our voting ensemble-based DL approach showed quite high performance for the diagnosis of middle ear disease. In clinical applications, this approach can provide a preliminary diagnosis of the patient's condition just before field experts make a diagnosis on otoscopic images. Thus, our proposed approach can help field experts to diagnose the disease quickly and accurately. In this way, clinicians can make the final diagnosis by integrating automatic diagnostic prediction with their experience.
LOOKING INTO THE ABYSS: MISE EN ABYME PRACTICES IN CONTEMPORARY ART
(2024.01.01) Aydin, A.; Uçar, M.
Mise en abyme, a French term derived from the heraldry, literally means 'to place in the abyss'. The term mizanabim, which was first conceptualized by Andr & eacute; Gide in his work Journals, is a formal method applied in various branches of art, primarily literature, by making use of visual metaphors. The examples of the method known as an application method based on the nesting of images in the modern and pre -modern period have also begun to be seen in contemporary art practices after modernity. This study aims to scrutinize and analyze the mise en abyme method, which is not well known and known about, through selected examples from contemporary art practices. For this purpose, some works of art have been identified in line with the data obtained through literature review on the subject. The identified works were examined by document analysis method and information on the subject was obtained as a result of interviews with some artists. While examining the samples of the works, the works were analyzed by using D & auml;llenbach's typology of mise en abyme. As a result of the analysis and evaluation, it was tried to be revealed that mise en abyme is a method used in contemporary art practices.
UÇURUMA BAKMAK: ÇAĞDAŞ SANATTA MİZANABİM UYGULAMALARI
(2024) Aydın, A.; Uçar, M.
Fransızca bir terim olan ve adını arma bilimi olan Heraldik’ten alan mizanabim (mise en abyme) tam olarak ‘uçuruma yerleştirmek’ anlamına gelir. İlk olarak André Gide’in Günlük adlı eserinde kavramsallaştırdığı mizanabim terimi görsel metaforlardan yararlanarak başta edebiyat olmak üzere çeşitli sanat dallarında uygulanan biçimsel bir yöntemdir. Modern ve modern öncesi dönemde görüntülerin iç içe yerleştirilmesine dayalı bir uygulama biçimi olarak bilinen yöntemin örnekleri modernite sonrası çağdaş sanat uygulamalarında da görülmeye başlanmıştır. Bu çalışma, hakkında fazla bilgi olmayan ve çok tanınmayan mizanabim yönteminin çağdaş sanat uygulamalarından seçilen örnekler üzerinden irdelenmesini ve analizini amaçlamaktadır. Bu amaç doğrultusunda konu ile ilgili olarak literatür tarama yoluyla elde edilen veriler doğrultusunda bazı sanat eserleri tespit edilmiştir. Tespit edilen eserler doküman analizi yöntemi ile incelenmiş ve konu ile ilgili bilgiler ise bazı sanatçılarla görüşme sonucu elde edilmiştir. Eser örnekleri incelenirken Dällenbach’ın mizanabim tipolojisinden yararlanılarak eserler analiz edilmiştir. Yapılan analiz ve değerlendirme sonucunda mizanabimin çağdaş sanat uygulamaları içerisinde kullanılan bir yöntem olduğu ortaya konulmaya çalışılmıştır.