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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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    Implementation of Non-periodic Sampling True Random Number Generator on FPGA
    (Soc Microelectronics, Electron Components Materials-Midem, 2014) Tuncer, Taner; Avaroglu, Erdinc; Turk, Mustafa; Ozer, A. Bedri
    Random numbers are essentially required for various cryptographic applications. It is ideal to use nondeterministic random number generators in cryptography field since they are able to generate high-quality random numbers. In this paper, a Ring Oscillator (RO) based True Random Number Generator (TRNG) that can be used in cryptographic applications was developed. In this system, random numbers are generated by non-periodic sampling. Sinusoidal iterator with chaotic behavior was used for generation of non-periodic sampling signals. In TRNG system; three different scenarios, each of which contains three inverters, with 25, 10 and 5 RO circuits were implemented on FPGA environment. Randomness tests of numbers that are generated by TRNG with non-periodic sampling were carried on according to the NIST 800.22 test suit. The results have shown that the proposed system can be used in the cryptographic systems.
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    A New Method for Hybrid Pseudo Random Number Generator
    (Soc Microelectronics, Electron Components Materials-Midem, 2014) Avaroglu, Erdinc; Tuncer, Taner; Ozer, A. Bedri; Turk, Mustafa
    Powerful cryptographic systems need qualified random numbers. Qualified random numbers need providing good statistical qualities, not predicting and not re-generating. The numbers generated by raw Pseudo Random Number Generators (PRNG) can be predicted when their seed value are detected or the functions used in the system are not complicated enough. Moreover, the stream generated repeats itself after its period is exhausted. Due to these shortcomings mentioned above, raw PRNGs are not suitable for the cryptographic applications. In order to eliminate these shortcomings, by adding an additional input to the raw PRNG system, a hybrid structure is suggested in this study. In the hybrid system, a chaotic attraction in order to generate pseudo random number and a TRNG system having 5 Ring Oscillator (RO) each of which includes 3 inverters as the additional input were used. The random numbers obtained from the suggested hybrid structure were exposed to the NIST 800.22 statistical tests and it is shown that hybrid system can be used in the cryptographic systems.
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    A novel chaos-based post-processing for TRNG
    (Springer, 2015) Avaroglu, Erdinc; Tuncer, Taner; Ozer, A. Bedri; Ergen, Burhan; Turk, Mustafa
    The usage of numbers generated by true random number generators is critical in cryptology field due to security reasons. On the other hand, generated raw numbers rarely have good statistical properties because entropy sources used in true random number generators can be influenced by environmental factors. Post-processing is required for TRNGs to overcome the shortcomings of generated raw numbers. In this paper, a chaos-based post-processing technique is proposed as an alternative to other post-processing techniques in the literature. Logistic map is used in post-processing to ensure that numbers generated by RO-based TRNG are high quality. Four different scenarios considering RO-based TRNG structure are examined in order to observe the effects of the logistic map. The proposed system is set on EP4CE115F29C7-based Altera FPGA board, and the statistical properties of generated numbers are tested according to NIST 800.22 test suit and TESTU01. The degree of non-periodicity of the developed system was inspected by employing scale index method. The generated series pursuant to the obtained results was non-periodic. The results suggest that logistic map can be used as post-processing.
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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.