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    COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers
    (Springer, 2022) Yıldırım, Muhammed; Eroğlu, Orkun; Eroğlu,Yeşim; Çınar, Ahmet; Cengil, Emine
    Coronavirus disease-2019 (COVID-19) is a serious infectious disease that is spreading rapidly all over the world. Scientists are looking for alternative diagnostic methods to detect and control the disease early. Artifcial intelligence applications are promising in the COVID-19 epidemic. This paper proposes a hybrid approach for diagnosing COVID-19 on chest X-ray images and diferentiation from other viral pneumonia. The model we propose consists of three steps. In the frst step, classifcation was made using the MobilenetV2, Efcientnetb0, and Darknet53 deep models. In the second step, the feature maps of the images in the Chest X-ray data set were extracted separately for each architecture using the MobilenetV2, Efcientnetb0, and Darknet53 architectures. NCA method was preferred to reduce the size of these feature maps obtained. The feature maps obtained after dimension reduction were classifed in the classic machine learning classifers. In the third step, the feature maps obtained from each architecture were combined. After dimension reduction was applied to these combined features by applying the NCA method, this feature map is classifed in the classifers. The model we proposed was tested on two diferent data sets. The accuracy values obtained in these data sets are 99.05 and 97.1%, respectively. The obtained accuracy values show that the model is successful.
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    Is it useful to use computerized tomography image-based artificial intelligence modelling in the differential diagnosis of chronic otitis media with and without cholesteatoma?
    (Elsevier B.V. All, 2022) Eroğlu, Orkun; Eroğlu, Yeşim; Yıldırım, Muhammed; Karlıdağ, Turgut; Çınar, Ahmet; Akyiğit, Abdulvahap; Kaygusuz, İrfan; Yıldırım, Hanefi; Keleş, Erol; Yalçın, Şinasi
    Cholesteatoma is an aggressive form of chronic otitis media (COM). For this reason, it is important to distinguish between COM with and without cholesteatoma. In this study, the role of artificial intelligence modelling in differentiating COM with and without cholesteatoma on computed tomography images was evaluated.