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    Automatic detection of keratoconus on Pentacam images using feature selection based on deep learning
    (Wiley, 2022) Firat, Murat; Cankaya, Cem; Cinar, Ahmet; Tuncer, Taner
    Today, corneal refraction, height, and thickness data, which are required in the diagnosis of keratoconus, can be obtained with corneal tomography devices. Pentacam four map display presenting this data is one of the most basic options in the diagnosis of keratoconus. In this article, an artificial intelligence-based method using Pentacam images is proposed to distinguish keratoconus from healthy eyes. Axial/sagittal curvature, back elevation, front elevation, and corneal thickness map images of a total of 341 keratoconus and 341 healthy corneas obtained from Inonu University ophthalmology clinic as the data set were given as input to AlexNet, one of the deep learning models, and the feature vectors of each image were obtained and combined. The most effective features in the determination of keratoconus were determined by applying ReliefF, minimum-redundancy-maximum-relevance (mRMR) and Laplacian algorithms, which are widely used in feature extraction algorithms, to the obtained feature vector. These features are classified using the support vector machine (SVM) classifier, which has high performance in binary classification. The accuracy, specificity, and sensitivity of keratoconus detection with the proposed method were found to be 98.53%, 99.01%, and 98.06%, respectively. The developed model can support the clinician to evaluate the features of the cornea and to detect keratoconus, which is difficult through subjective assessments, especially in the subclinical and early stages of the disease.
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    Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model
    (Elsevier Ireland Ltd, 2021) Eroglu, Yesim; Yildirim, Kadir; Cinar, Ahmet; Yildirim, Muhammed
    Background and objective: Vesicoureteral reflux is the leakage of urine from the bladder into the ureter. As a result, urinary tract infections and kidney scarring can occur in children. Voiding cystourethrography is the primary radiological imaging method used to diagnose vesicoureteral reflux in children with a history of recurrent urinary tract infection. Besides the diagnosis of reflux, it is graded with voiding cystourethrography. In this study, we aimed to diagnose and grade vesicoureteral reflux in Voiding cystourethrography images using hybrid CNN in deep learning methods. Methods: Images of pediatric patients diagnosed with VUR between 2016 and 2021 in our hospital (Firat University Hospital) were graded according to the international vesicoureteral reflux radiographic grading system. VCUG images of 236 normal and 992 with vesicoureteral reflux pediatric patients were available. A total of 6 classes were created as normal and graded 1-5 patients. Results: In this study, a hybrid-based mRMR (Minimum Redundancy Maximum Relevance) using CNN (Convolutional Neural Networks) model is developed for the diagnosis and grading of vesicoureteral re flux on voiding cystourethrography images. Googlenet, MobilenetV2, and Densenet201 models are used as a part of the hybrid architecture. The obtained features from these architectures are examined in concatenating process. Then, these features are classified in machine learning classifiers after optimizing with the mRMR method. Among the models used in the study, the highest accuracy value was obtained in the proposed model with an accuracy rate of 96.9%. Conclusions: It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images. (c) 2021 Elsevier B.V. All rights reserved.
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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.
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    UAV Traffic Patrolling via Road Detection and Tracking in Anonymous Aerial Video Frames
    (Springer, 2019) Karaduman, Mucahit; Cinar, Ahmet; Eren, Haluk
    Unmanned Aerial Vehicles (UAV) have gained great importance for patrolling, exploration, and surveillance. In this study, we have estimated a route UAV to follow, using aerial road images. In the experimental setup, for estimation, test, and validation stages, anonymous aerial road videos have been exploited, meaning a special image database was not produced for this simulation approach. In the proposed study, road portion is initially detected. Two methods are utilized to help road detection, which are k-Nearest Neighbor and Hough transformation. To form a decision loop, both results are matched. If they match each other, they are fused using spatial and spectral schemes for the comparison purpose. Once road area is detected, the road type classification is realized by Fuzzy approach. The resultant image is utilized to estimate route, over which the UAV have to fly towards that direction. In the simulation stage, an anonymous video stream previously captured by UAV is experimented to assess the performance of the underlying system for different roads. According to the implementation results, the proposed algorithm has succeeded in finding all the trial roads in the given aerial images, and the proportion of all the estimated road-portion to actual road pixels for all the images is averagely calculated as %95.40. Eventually, it is shown that UAV has followed the correct route, which is estimated by proposed approach, over the specified road using assigned video frames, and also performances of spatial and spectral fusion results are compared.