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    Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma
    (Aves, 2023) Eroglu, Orkun; Eroglu, Yesim; Yildirim, Muhammed; Karlidag, Turgut; Cinar, Ahmet; Akyigit, Abdulvahap; Yalcin, Sinasi
    BACKGROUND: In this study, we aimed to compare the success rates of computed tomography image-based artificial intelligence models and magnetic resonance imaging in the diagnosis of preoperative cholesteatoma. METHODS: The files of 75 patients who underwent tympanomastoid surgery with the diagnosis of chronic otitis media between January 2010 and January 2021 in our clinic were reviewed retrospectively. The patients were classified into the chronic otitis group without cholesteatoma (n = 34) and the chronic otitis group with cholesteatoma (n = 41) according to the presence of cholesteatoma at surgery. A dataset was created from the preoperative computed tomography images of the patients. In this dataset, the success rates of artificial intelligence in the diagnosis of cholesteatoma were determined by using the most frequently used artificial intelligence models in the literature. In addition, preoperative MRI were evaluated and the success rates were compared. RESULTS: Among the artificial intelligence architectures used in the paper, the lowest result was obtained in MobileNetV2 with an accuracy of 83.30%, while the highest result was obtained in DenseNet201 with an accuracy of 90.99%. In our paper, the specificity of preoperative magnetic resonance imaging in the diagnosis of cholesteatoma was 88.23% and the sensitivity was 87.80%. CONCLUSION: In this study, we showed that artificial intelligence can be used with similar reliability to magnetic resonance imaging in the diagnosis of cholesteatoma. This is the first study that, to our knowledge, compares magnetic resonance imaging with artificial intelligence models for the purpose of identifying preoperative cholesteatomas.
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    Detection and classification of glioma, meningioma, pituitary tumor, and normal in brain magnetic resonance imaging using deep learning-based hybrid model
    (Springer International Publishing, 2023) Yildirim, Muhammed; Cengil, Emine; Eroglu, Yeşim; Cinar, Ahmet
    Pituitary, meningioma, and glioma tumors are the primary widespread brain tumors. Treatment algorithms for these tumors may differ from each other. For this reason, the detection and typing of brain tumors are necessary to determine the appropriate treatment quickly. Since imaging findings vary, even experts often have difficulties in classifying brain tumors. In this study, a convolutional neural network-based hybrid model is developed to classify glioma, meningioma, pituitary tumors, and normal brain magnetic resonance images. In the proposed hybrid model, features are obtained using pre-trained Efficientnetb0 and Shufflenet architectures. Also, the images in the existing dataset are improved, such as colored and used as the second database. With the proposed two pre-trained models, the features of individual images from two datasets are extracted. Later, these features are concatenated, and the best of these features are selected using the mRMR feature reduction method and classified using the Support Vector Machine (SVM) classifier. The proposed method achieved better results than the previously trained Efficientnetb0 and Shufflenet architectures. The accuracy value of the proposed hybrid model is 95.4%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.
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    Diagnosis of periventricular leukomalacia in children with artificial intelligence-based models developed using brain magnetic resonance images
    (Springer London Ltd, 2023) Eroglu, Yesim; Yildirim, Muhammed; Cinar, Ahmet
    Periventricular leukomalacia is periventricular white matter damage that develops due to hypoxia and ischemia of the brain. It is one of the leading causes of neurological and developmental problems in children that will affect their future lives. Therefore, the correct diagnosis is important for giving the appropriate treatment. The main imaging method used in the diagnosis is magnetic resonance imaging (MRI). In this study, we evaluated the detectability of periventricular leukomalacia with artificial intelligence models in MRIs in children. In the study, two new artificial intelligence-based models are proposed to classify brain MRIs. The first proposed model consists of 19 layers, and this new model was more successful than previously trained deep models for classifying MRIs. In addition, the number of layers is lower than the models accepted in the literature. In the second model we proposed, the features were taken from our first model and optimized with the neighborhood component analysis (NCA) method, and then classified in the wide neural network. The accuracy values obtained in the models we have proposed are 94.62 and 98.92%, respectively. These accuracy values show that our proposed model is successful in classifying MRIs.
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    Prediction of Pentacam image after corneal cross-linking by linear interpolation technique and U-NET based 2D regression model
    (Pergamon-Elsevier Science Ltd, 2022) Firat, Murat; Cinar, Ahmet; Cankaya, Cem; Firat, Ilknur Tuncer; Tuncer, Taner
    Keratoconus is a common corneal disease that causes vision loss. In order to prevent the progression of the disease, the corneal cross-linking (CXL) treatment is applied. The follow-up of keratoconus after treatment is essential to predict the course of the disease and possible changes in the treatment. In this paper, a deep learningbased 2D regression method is proposed to predict the postoperative Pentacam map images of CXL-treated patients. New images are obtained by the linear interpolation augmentation method from the Pentacam images obtained before and after the CXL treatment. Augmented images and preoperative Pentacam images are given as input to U-Net-based 2D regression architecture. The output of the regression layer, the last layer of the U-Net architecture, provides a predicted Pentacam image of the later stage of the disease. The similarity of the predicted image in the final layer output to the Pentacam image in the postoperative period is evaluated by image similarity algorithms. As a result of the evaluation, the mean SSIM (The structural similarity index measure), PSNR (peak signal-to-noise ratio), and RMSE (root mean square error) similarity values are calculated as 0.8266, 65.85, and 0.134, respectively. These results show that our method successfully predicts the postoperative images of patients treated with CXL.