Fakülteler / Faculties

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    Computer-Aided Breast Cancer Diagnosis from Thermal Images Using Transfer Learning
    (2020) Cabioglu, Cagri; Ogul, Hasan
    Breast cancer is one of the prevalent types of cancer. Early diagnosis and treatment of breast cancer have vital importance for patients. Various imaging techniques are used in the detection of cancer. Thermal images are obtained by using the temperature difference of regions without giving radiation by the thermal camera. In this study, we present methods for computer aided diagnosis of breast cancer using thermal images. To this end, various Convolutional Neural Networks (CNNs) have been designed by using transfer learning methodology. The performance of the designed nets was evaluated on a benchmarking dataset considering accuracy, precision, recall, F1 measure, and Matthews Correlation coefficient. The results show that an architecture holding pre-trained convolutional layers and training newly added fully connected layers achieves a better performance compared with others. We have obtained an accuracy of 94.3%, a precision of 94.7% and a recall of 93.3% using transfer learning methodology with CNN.
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    Applications of Deep Learning Techniques to Wood Anomaly Detection
    (2022) Celik, Yaren; Guney, Selda; Dengiz, Berna; Xu, J; Altiparmak, F.; Hassan, MHA; Marquez, FPG
    Wood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. Some difficulty and complexity of production processes result in high variability of raw materials such as a wide range of visible structural defects that must be checked by specialists on line or of line. These issues are not only difficult and biased in manual processes, but also less effective and misleading. To overcome the drawbacks of the manual quality control processes, machine vision-based inspection systems are in great of interest recently for quality control applications. In this study, the wood anomaly has been detected by using deep learning. As it will be a distinction-based method on image processing, the Convolution Neural Network (CNN), which is one of the most suitable methods, has been used for anomaly detection. In addition, it will be tried to obtain the most suitable one among different CNN architectures such as ShuffleNet, AlexNet, GoogleNet for the problem. MobileNet, SqueezeNet, GoogleNet, ShuffleNet among considered methods show promising results in classifying normal and abnormal wood products.
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    A real-time approach to recognition of Turkish sign language by using convolutional neural networks
    (2021) Guney, Selda; 0000-0002-0573-1326
    Sign language is a form of visual communication used by people with hearing problems to express themselves. The main purpose of this study is to make life easier for these people. In this study, a data set was created using 3200 RGB images for 32 classes (32 static words) taken from three different people. Data augmentation methods were applied to the data sets, and the number of images increased from 3200 to 19,200, 600 per class. A 10-layer convolutional neural network model was created for the classification of the signs, and VGG166, Inception, and ResNet deep network architectures, which are deep learning methods, were applied by using the transfer learning method. Also, the signs are classified using the support vector machines and k-nearest neighbor methods, which are the traditional machine learning methods, by using features obtained from the last layer of the convolutional neural network. The most successful method was determined by comparing the obtained results according to time and performance ratios. In addition to these analyses, an interface was developed. By using the interface, the static words belonging to Turkish sign language (TSL) are translated into real-time written language. With the real-time system designed, its success in recognizing the static words of TSL signs and printing its prediction on the computer screen was evaluated.